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",-.01456787:('&,%$#"R c  A)*  , E*eijk R$ w՝F22$oTp ˒2b$+*KFB 22$j9wxV422$G);1/bAl#22$I{Oo*p>2$2$2$22$`R yuy!.22$Azxw`E=b622$Πhk_F:y(2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2b$VD\idxm 2$2$2$2$2$2$2$2$2$2$2b$YJT5(7D2$2$2$2$2$2b$LMa) ?{:2b$s8Pr%=* A2b$'*B&7s J2b$ʩ2R$Յ&%u+J8B%V<2R$j %$#=2R$b֌3K2R$Xeq|X"#Q2R$# G 2R$N^Ӵa qM.ZJ 2R$Eu3:5LJ$VMH 22$rzUZ2 2 0e0e A@A5%8c8c     ?1d0u0@Ty2 NP'p<'p@A)BCD|E? ff3f( @8d g4BdBdv 0pm`ppp@ <4BdBd` 0,6 g4&d&dv 0pp8 ph<4!d!d` 0,6 \ ʚ;ʚ;<4dddd{ 0r0___PPT10 2___PPT9/ 0?^+ JChap4 Inductive Learning Zhongzhi ShiO =LAdvanced Computing Seminar Data Mining and Its Industrial Applications  Chapter 4  Inductive Learninghl ,b,bbf6f P Zhongzhi Shi, Markus Stumptner, Yalei Hao, Gerald Quirchmayr Knowledge and Software Engineering Lab Advanced Computing Research Centre School of Computer and Information Science University of South Australia )L>n<>bBb                !0>Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm Summary8fx ddX` f sBasic Concepts`  ~Data: Store on any media with certain format Information: Assign meaning to concrete data knowledge: Refine from informationlxf$*b$ f$#b$ f$b$  -"Data Mining vs Knowledge Discovery##b &    IData mining Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. nnn d`b $/d`$' q  .Data Mining: A KDD Process(b b   ^Data mining core of knowledge discovery processJ0Z bb#bb 0 lData Warehouse Process  m Macro Picture   nDetailed picture  *Knowledge Representation`  IProduction system Frame Semantic networks First order logic Ontology &FxxJ` J /Production System`  Rules IF (conditions) Then (conclusions) If ( animal has wing) and (animal can fly) Then (animal is a bird) 2xzxx`  e Production Systemd&     f Frame Structure0de&    g Semantic Networks0de&     0First Order Logic`  Student(John) Teacher(Markus) Father(x,y) Father(y,z) Grandfather(x,z):-Father(x,y),Father(y,z) If ( animal has wing) and (animal can fly) Then (animal is a bird) ({x6xb  hOntology `  @ Semantic Web: Ontology OWL Ontology schema Description Logic&x0xA` A 2Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryHfx dddG` f 1The Essence of Learning`  aLearning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time. [Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   N    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  !              %Example of the Ordering of Hypotheses&&` & `  ~Version Space Search `   Version Space Example`  `  Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  9     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  )  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`   Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T Which attribute is best?64" 9  E3   2bb$bbbbbjbbbjb% b  b  b  b  b  b  j  b  b  j  b  b  j & b  b  b  b  j  b  b  j  b  b  j  b  b  b bbjbbjbbjbbjbbbbbg    >     5            3 Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9         )     Entropy and Information Gain`     What is c4.5?`   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes.`   ! Running c4.5 `   On cunix.columbia.edu ~amr2104/c4.5/bin/c4.5  u  f filestem c4.5 expects to find 3 files filestem.names filestem.data filestem.test\&+` &` ` +`                "File Format: .names`   The file begins with a comma separated list of classes ending with a period, followed by a blank line E.g, >50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                 $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]`  L %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     B & c4.5 Output `   The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` f  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c&  u (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c 5 )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS`  S 5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd` f Inductive Learning`   ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationlxBa`&ae`a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc    H                       =      -       Ripper`  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uc u Ripper`  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c&!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summary`  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesx`  Y References b   pZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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U Selection  b      `1?` VZ [ Data Mining  f   !  `1?  y ^Pattern Evaluation b  jB " BDԔ? vB # NDԔ? jB $@ BDԔ?  vB % NDԔ? |B & TDԔ? |B '@ TDԔ?|B ( TDԔ?P B )  fDo??P  P B * # lDo??P P "  C W>d?f P 6KnowledgeImpactppH  0޽h ? G@@(3ey___PPT10Y+D=' = @B +)  i(  ~  s *8 ,@mp     <A ??0`8 $D0z  &V    V ,$D0l2  <Ԕ R P   <H &V 7 Meta data management Data access Systems Integration88b 7 H  0޽h ? ̙33  NF(  ~  s *@ ,@mp  @  8 pP@ 0`  0 ̙ @` ,$D0 w-Data Mining Approach to Data Warehouse Design..` .  ` @X   ` @X ,$D0  s *  @X  _Desired star schemab  x"  H?` P M p   p ,$D0  T  ?p ; /Attribute Width Type NULL allowed Name Key4 & b'b 0 &   T܃ ? g 6Numeric Maximum Minimum Average Standard deviation4/b0b 7 ,   T, ? L <Text fields Number of spaces Numerals used Average length4 1 b2b = xB   H?  P N      lB   <? 0 0lB  <?P lB  <? >  P@   P@,$D0  N$?m@@  Bb    T ?l@ `Designed Star Schemab  h  c $A ?? P9  x" B H?P ppG @@ @  @@ @,$D0xB  H?P 0 @  Nd?@@  Bb    T ?  Y Mapping Rulesb  x" B H? p H  0޽h ? ̙33  T(  ~  s *j@ ,@mp  @   ZA ? ?`2 H  0޽h ? ̙33   h`(    3 rHgֳgֳ ? ,     3 r@gֳgֳ ? ,   <$ 0  H  0޽h ? G@@(3e   h`(    3 rT@gֳgֳ ? ,     3 rT@gֳgֳ ? ,  <$ 0 @ H  0޽h ? G@@(3e  LD(  ~  s *@ ,@mp  @   0@` Y V MYCIN $ = IF THEN (ELSE $ $ = AND $ $ = OR | $ $ = $ $ = ) | $ $ = $W    8+ B 6 * 9>    H H  0޽h ? ̙33M  (  ~  s *'@ ,@mp  @   0)@ o FRAME FRAME-NAME SLOT-NAME-1: ASPECT-11 ASPECT-VALUE-11 ASPECT-12 ASPECT-VALUE-12 ASPECT-1m AWPECT-VALUE-1m ...... ...... SOLT-NAME-n: ASPECT-n1 ASPECT VALUE-n1 ASPECT-n2 ASPECT-VAPECT-VALUE-n2 ASPECT-n1 ASPECT-VALUE-n12 a  H  0޽h ? ̙33     F (  ~  s *2@ ,@mp  @   03@ & node: objects arc: relationships0' &c$c >     2   `??0 P p 2   `?? @` @ 2   `??P  2    `??  2    `?? 0 P 2    `?? 0 P 2    `??P 2    `?? 0  B @  `D?? P B @  `D??  B @  `D??` pP B @  `D?? ` B   `D?? p B   `D?? p B   `D??@ H  0޽h ? ̙33   h`(    3 r?@gֳgֳ ? ,  @   3 r@@gֳgֳ ? ,  <$ 0 @ H  0޽h ? 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G@@(3e   h`(    3 r 8 C A=xD:\reports\Personal\Proposals\Hanoi\bookps\VS\vs_exam_4.JPGP H 8 0޽h ? ̙33  <6(  <~ < s *@ ,p  @ x < c $x@ , @ H < 0޽h ? ̙33   f^x(  x x 3 rD@gֳgֳ ? ,  @  x T gֳgֳ ?  ` lAssume consistent training data Noise-sensitive Comments on version space though not practical in most real-world learning problems, they provide a good deal of insight into the logical structure of hypothesis space NJ nK FiJb `   H x 0޽h ? G@@(3e    |3(  | | 3 r@gֳgֳ ? ,  @ - F 2 |   r | B.1? r | B.1?;   | Z gֳgֳ?   [VS1(` h    | Zgֳgֳ?6   [VS2(` h     | Zgֳgֳ?  2 ZS1(` h     | Zgֳgֳ?F e2 ZS2(` h     | Zgֳgֳ?   ZG1(` h     | Z|gֳgֳ? U ZG2(` h   ,  | Z gֳgֳ?   G1 2db j k c j   , | Z gֳgֳ?  S1 2db j k c j   - | Zgֳgֳ?    VS1 2db j k c j   f2 | 61? t, f2 | 61? , \ f2 | 61? | f2 | 61?T <f2 | 61? | Lf2 | 61?T  LH | 0޽h ? G@@(3eh        (    3 r gֳgֳ ? ,     Tgֳgֳ ?    Conceptional each new piece of information new version space Practical parallel ambiguous, inconsistent data, background domain theories  nK2 Fi nKC Fi b ` a ` b C`    eF  `1   `1l  <1?d    Z gֳgֳ? b   KVSM`   lB  <1? ` ` lB  <1?    Zgֳgֳ?6  [VSI(` h      Zgֳgֳ?6  ]VSn(` h  lB   <1?` ` lB   <1?0 `     BCADEF1?@@  @ 1H  0޽h ? G@@(3e,      T (    3 rgֳgֳ ? ,     3 rgֳgֳ ? ,n   " @`  Zhgֳgֳ?2  ` Polyhedron a      Zgֳgֳ? 2  ^Spheroid a     Z@gֳgֳ? Pany-size `     Ztgֳgֳ?   [Largea     Zgֳgֳ?J  [Smalla      `gֳgֳ? q}  ZCubea      `m@gֳgֳ? Q  ]Pyramida      `@r@gֳgֳ? a] a Octoploid a    dB @ <1?pdB  <1?p dB  <1?0  dB @ <1?0  dB  <1?0  dB @ <1?@ dB  <1? `H  0޽h ? G@@(3e   6(    3 rdz@gֳgֳ ? ,  @ 0  3 r@gֳgֳ ? , \$0!0 @ * OH  0޽h ? G@@(3e   (    3 r@gֳgֳ ? ,  @   Ṱ@gֳgֳ ?  ` HDefinition any basis for choosing one generalization over another any factor that influences the definition or selection of inductive hypotheses Representational bias lauguage, language implementation, primitive terms Procedural (algorithmic) bias order of traversal of the states in the space defined by a representational bias nK Fi nK3 Fi nKQ Fi b ` b 3` b Q ` &   H  0޽h ? G@@(3e   T L (  x  c $t@ ,  @ ^B  6D>`@`R  s *P  6̵@@ _Program `    6@ @`  e Training set `     6@ p  ^Search `    <   a Knowledge   `   LB   c $D` `LB   c $Dp @ p RB   s *D p RB   s *D` RB   s *D@@  <@p9  ZBias `    <@ ,  gTraining Examples `    <@ @ ` Hypothesis  `   H  0޽h ? ̙33   aY (    3 r @gֳgֳ ? ,  @   T@gֳgֳ ?  ` g)Real-world domains have potentially hundreds of features and sources of data Why is bias selection important? improve the predictive accuracy of the learner improve performance goals Selection: static vs. dynamic Evaluation: basis for bias selection online and empirical vs. offline and analyticaln nKI FiC nK0 Finb I` b b b b &b ` ` `  * H  0޽h ? G@@(3e   0:(    3 r\@gֳgֳ ? ,  @ 4  Tt.'gֳgֳ ?  ` Bias shifting bias selection occurs again after learning has begun useful when the knowledge for bias selection is not available prior to learning, but can be gathered during learning Multi-tierd bias make embedded biases explicit! reduce the cost of system and knowledge engineering flexible system design, conceptual simplicity Characterize learning as search within multiple tiers! nK Fi nK Fi7 nKb ` b ` b` 7` &   H  0޽h ? G@@(3e;   @ !c(    3 r$@gֳgֳ ? ,  @ ]F \  `tl  <1? D \l  <1?tt l  <1?t < lB  <1? 0 lB  B <1?P    Z'gֳgֳ? Z  NL(H)b      Z'gֳgֳ?V j2 Q KHb      T'gֳgֳ? @  RP(l(H))) b   l   <1?t  l  <1? \ l  <1?T  l  <1?<   Z'gֳgֳ?f2  T P(l(L(H))) b     Z'gֳgֳ? 2  QL(L(H))b   lB  <1? 0 p lB B <1?  p   Z'gֳgֳ?F   T L(P(l(H))) b     Z'gֳgֳ?R  W P(l(P(l(H))))b   lB  <1? p lB B <1? p   Z'gֳgֳ?j   eRepresentational Bias Spaceb     Z'gֳgֳ?j   _Procedural Bias Spaceb     Z'gֳgֳ?jV Q ZHypothesis Spaceb     Z'gֳgֳ?& Z eProcedural Meta-Bias Spacesb     Z'gֳgֳ?ZA k!Representational Meta-Bias Spaces""b  " lB  <1?  @lB  <1?p p@lB  B <1?@AlB ! <1?Q @H  0޽h ? G@@(3e   h`P(    3 r'gֳgֳ ? ,  '   3 rl'gֳgֳ ? ,  <$ 0 ' H  0޽h ? G@@(3et    `(    3 rdT$gֳgֳ ? ,  $   TU$gֳgֳ ?  `f^V___PPT980   "1966 Hunt, Marin, Stone: CLS 1983 Quinlan: ID3 1986 Schlimmer, Fisher: ID4 Incremental learning Utgoff: ID5 Quinlan: C4.5, C5 po    oc c c                     @`H  0޽h ? G@@(3e  zrpL (  L~ L s *f$ ,@m  $  L c $o$ ,P` $ .5 ^ F @ L @lB L <D1?@lB L <D1?@lB L <D1?H L 0޽h ? ̙33   (    3 ru$gֳgֳ ? ,  $   TPv$gֳgֳ ?  ` "FDecision tree each internal node tests an attribute each branch corresponds to attribute value each leaf node assigns a classification Decision trees are inherently disjunctive, since each branch leaving a decision node corresponds to a separate disjunctive case. Decision trees can be used to represent disjunctive concepts. 0 n<Zy0 n7Z0 n<Zcycc a  G  @`H  0޽h ? G@@(3e   zr (    3 ry$gֳgֳ ? ,  $   T$gֳgֳ ? ` JThe CLS algorithm starts with an empty decision tree and gradually refines it, by adding decision nodes, until the tree correctly classifies all the training instances. The algorithm operates over a set of training instances, C, as follows: If all instances in C are positive, then create a YES node and halt. If all instances in C are negative, create a NO node and halt. Otherwise, select (using some heuristic criterion) an attribute, A, with values v1,& ,vn and create the decision tree. Partition the training instances in C into subsets C1,& ,Cn according to the values of V. Apply the algorithm recursively to each of the sets Ci.0 n<ZC0 n<Z#0 n<Z80 n<ZccaZ  X  R    @`H  0޽h ? G@@(3e    3(    3 r<$gֳgֳ ? ,  $ -  T$gֳgֳ ?  ` ID3 algorithm build decision tree based on training objects with known class labels to classify testing objects rank attributes with information gain measure minimal height the least number of tests to classify an object0 n<Z0 n7Z00 n2Zcc0a   @`H  0޽h ? G@@(3e  @<(  @~ @ s *t$ ,@m   $ ~ @ s *Ĥ$ ,] $ H @ 0޽h ? ̙33  jbD(  D~ D s *$ ,@m#  $ ~ D s *d$ , $  D C A>vD:\reports\Personal\Proposals\Hanoi\bookps\ID3\dt_exam.JPGt0"H D 0޽h ? ̙33  H<(  H~ H s *<$ ,p  $ ~ H s *$ , $ H H 0޽h ? ̙33  P<(  P~ P s *$ ,PP  $ ~ P s *0$ ,8 $ H P 0޽h ? ̙33  T6(  T~ T s *$ ,@m  $ x T c $$ ,@0 $ H T 0޽h ? ̙33  d\X(  X~ X s *$ ,@m  $ x X c $L$ ,0  $  X C A?vD:\reports\Personal\Proposals\Hanoi\bookps\ID3\entropy.JPG H X 0޽h ? ̙33^  \(  \~ \ s *H$ ,@m#  $ x \ c $$ ,` $ ` \ c $A@ ?? 0C  H \ 0޽h ? ̙33   `6(  `~ ` s *Hxp ,@m   p x ` c $yp , p H ` 0޽h ? ̙33   rj0(    3 rl}pgֳgֳ ? ,  p   Tfpgֳgֳ ?  0 @S contains si tuples of class Ci for i = {1, & , m} Information measures info required to classify any arbitrary tuple Entropy of attribute A with values {a1,a2,& ,av} Information gained by branching on attribute A  n< ckck}ckckck4c       Q  ,  3   @``  c $A h?? 0 h`  c $A i??P   i`  c $A j??p ` jH  0޽h ? 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",-.01456787:('&,%$#"R c  A)*  , E*eik  R$ w՝F2$oTp ˒b$+*KFB 2$j9wxV42$G);1/bAl#2$I{Oo*p>$$$2$`R yuy!.2$Azxw`E=b62$Πhk_F:y($$$$$$$$$$$$$$$$$$$$$b$VD\idxm $$$$$$$$$$b$YJT5(7D$$$$$b$LMa) ?{:b$s8Pr%=* Ab$'*B&7s Jb$ʩR$Յ&%u+J8B%V<R$j %$#=R$b֌3KR$Xeq|X"#QR$# G R$N^Ӵa qM.ZJ R$Eu3:5LJ$VMH 2$rzUZ2  0e0e A@A5%8c8c     ?1d0u0@Ty2 NP'p<'p@A)BCD|E? ff3f( @8d g4BdBdv 0pm`ppp@ <4BdBd` 0,g4&d&dv 0pp8 ph<4!d!d` 0,\ ʚ;ʚ;<4dddd{ 0r0___PPT10 2___PPT9/ 0?^+ JChap4 Inductive Learning Zhongzhi ShiO =LAdvanced Computing Seminar Data Mining and Its Industrial Applications  Chapter 4  Inductive Learninghl ,b,bbf6f& O  Zhongzhi Shi, Markus Stumptner, Yalei Hao, Gerald Quirchmayr Knowledge and Software Engineering Lab Advanced Computing Research Centre School of Computer and Information Science University of South Australia )L>n<>bBb                 !0>Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm Summary8fx ddX`e  sBasic Concepts`  ~Data: Store on any media with certain format Information: Assign meaning to concrete data knowledge: Refine from informationlxf$*b$ f$#b$ f$b$~  o-"Data Mining vs Knowledge Discovery##b 4     IData mining Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. nnn d`b $/d`@' p    .Data Mining: A KDD Process(b b   ^Data mining core of knowledge discovery processJ0Z bb#bb/  lData Warehouse Process`  m Macro Picture`   nDetailed picture`  *Knowledge Representation`  IProduction system Frame Semantic networks First order logic Ontology &FxxJ`I  /Production System`  Rules IF (conditions) Then (conclusions) If ( animal has wing) and (animal can fly) Then (animal is a bird) 2xzxx`  e Production Systemd(    ` f Frame Structure0de(   ` g Semantic Networks0de(    ` 0First Order Logic`  Student(John) Teacher(Markus) Father(x,y) Father(y,z) Grandfather(x,z):-Father(x,y),Father(y,z) If ( animal has wing) and (animal can fly) Then (animal is a bird) ({x6xb  hOntology `  @ Semantic Web: Ontology OWL Ontology schema Description Logic&x0xA`@  2Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryHfx dddG`e  1The Essence of Learning`  aLearning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time. [Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb`a  tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datat%xxVx%`bbC{  7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs#  vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.BxxxxabN:   N     wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `   The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjbNA       :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb Y  Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b 7  Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`  ` Inductive Learning $a`  ` {Inductive Learning Method $a`  ` Inductive Learning Method $a`  ` Inductive Learning Method $a`  ` Inductive Learning Method $a`  ` }Best-Hypothesis `  ` 3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0`e  |Hypothesis Space`  ` !Training Examples for Enjoy Sport""`!  \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbbB 4     )is more_general_than_or_equal_to relation**`)  Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  !               %Example of the Ordering of Hypotheses&&`%  ` ~Version Space Search `  ` Version Space Example`  ` Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bbU  Candidate-elimination algorithm `  ` Candidate-elimination algorithm `  ` )Converging Boundaries of the G and S sets**`)   b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```N    1   New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbbN  9      3Remarks on Version Space and Candidate-Elimination44`3  The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`bjS   (   '  Drawbacks of Version Space `  ` Version-Space Merging`  ` Version-Space Merging`  ` IVSM Examples `   any-shape   b   IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*bp5                                                                         Bias `  ` Biasa  ` Bias Selection & Evaluation `  ` Multi-Tiered Bias System `  ` Multi-Tiered Bias Search Space`        !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~` 4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd`e  Decision Tree Learning`  ` Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`  ` CLS learning algorithm`  `  ID3 Approach `   `  Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbb(Q    Decision Tree Example`   b 'Appropriate problems for decision Trees((`'  Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@`?  Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T Which attribute is best?64" 9  E3   2bb$bbbbbjbbbjb% b  b  b  b  b  b  j  b  b  j  b  b  j & b  b  b  b  j  b  b  j  b  b  j  b  b  b bbjbbjbbjbbjbbbbb.g      >     5             2  Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb@#  N =  !Information Gain Search Heuristic""`!  Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bbZt Q 6     Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    8           )      Entropy and Information Gain`  `   What is c4.5?`    c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes.`   ! Running c4.5 `    On cunix.columbia.edu ~amr2104/c4.5/bin/c4.5  u  f filestem c4.5 expects to find 3 files filestem.names filestem.data filestem.test\&+` &` ` +`                    "File Format: .names`   The file begins with a comma separated list of classes ending with a period, followed by a blank line E.g, >50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                    $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]` K  %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     A  & c4.5 Output `    The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` v  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c4  t  (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c4  )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS` R  5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd`e  Inductive Learning`  ` ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationlxBa`&ae`a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc      H                       =      *         Ripper`  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uct  Ripper`  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c(!   6Outline` eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPddf Summary` Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesx` Y References b   pZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. L&Vb!bb4  M     ,       &  =  "   5   W  M rwww.UniSA.edu.au/~shizz/,b(f(b       !0 Questions?!4 Z g  Hb  /Ml5 (  #  T "ֳ"ֳ ?!  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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb`a  tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC{  7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs#  vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.BxxxxabN:   N     wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `   The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjbNA       :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb Y  Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b 7  Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0`e  |Hypothesis Space`   !Training Examples for Enjoy Sport""`!  \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbbB 4     )is more_general_than_or_equal_to relation**`)  Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  !               %Example of the Ordering of Hypotheses&&`%  `  ~Version Space Search `   Version Space Example`  `  Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bbU  Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**`)   b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```N    1   New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbbN  9      3Remarks on Version Space and Candidate-Elimination44`3  The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`bjS   (   '  Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b   IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*bp5                                                                         Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd`e  Decision Tree Learning`   Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `     Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbb&Q    Decision Tree Example`   b 'Appropriate problems for decision Trees((`'  Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@`?  Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate dDArial Unicode MST ܖ 0ܖ"DWingdings 3e MST ܖ 0ܖDArialngs 3e MST ܖ 0ܖ"De0}fԚngs 3e MST ܖ 0ܖDMingLiUs 3e MST ܖ 0ܖ1DjwiԚLiUs 3e MST ܖ 0ܖDwiSO_GB23123e MST ܖ 0ecision tree T Which attribute is best?64" 9  E3   2bb$bbbbbjbbbjb% b  b  b  b  b  b  j  b  b  j  b  b  j & b  b  b  b  j  b  b  j  b  b  j  b  b  b bbjbbjbbjbbjbbbbb.g      >     5             2  Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb@#  N =  !Information Gain Search Heuristic""`!  Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bbZt Q 6     Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    8           )      Entropy and Information Gain`     What is c4.5?`    c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes.`   ! Running c4.5 `    On cunix.columbia.edu ~amr2104/c4.5/bin/c4.5  u  f filestem c4.5 expects to find 3 files filestem.names filestem.data filestem.test\&+` &` ` +`                    "File Format: .names`   The file begins with a comma separated list of classes ending with a period, followed by a blank line E.g, >50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                    $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]` K  %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     A  & c4.5 Output `    The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` t  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c4  t  (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c4  )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS` R  5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd`e  Inductive Learning`   ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationlxBa`&ae`a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc      H                       =      *         Ripper`  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uct  Ripper`  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c&!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summary`  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesx`  Y References b   pZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb`a  tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC{  7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs#  vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.BxxxxabN:   N     wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `   The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjbNA       :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb Y  Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b 7  Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0`e  |Hypothesis Space`   !Training Examples for Enjoy Sport""`!  \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbbB 4     )is more_general_than_or_equal_to relation**`)  Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  !               %Example of the Ordering of Hypotheses&&`%  `  ~Version Space Search `   Version Space Example`  `  Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bbU  Candidate-elimination algorithm `   Candidate-elimination algorithm `ܖ@1DHelvetica3e MST ܖ 0ܖh "DMonotype SortsST ܖ 0ܖDHelvetica BlackT ܖ 0ܖ A.  @n?" dd@  @@`` 66`(( (o   )Converging Boundaries of the G and S sets**`)   b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```N    1   New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbbN  9      3Remarks on Version Space and Candidate-Elimination44`3  The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`bjS   (   '  Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b   IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*bp5                                                                         Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd`e  Decision Tree Learning`   Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `     Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbb&Q    Decision Tree Example`   b 'Appropriate problems for decision Trees((`'  Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@`?  Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T Which attribute is best?64" 9  E3   2bb$bbbbbjbbbjb% b  b  b  b  b  b  j  b  b  j  b  b  j & b  b  b  b  j  b  b  j  b  b  j  b  b  b bbjbbjbbjbbjbbbbb.g      >     5             2  Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb@#  N =  !Information Gain Search Heuristic""`!  Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bbZt Q 6     Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    8           )      Entropy and Information Gain`     What is c4.5?`    c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes.`   ! Running c4.5 `    On cunix.columbia.edu ~amr2104/c4.5/bin/c4.5  u  f filestem c4.5 expects to find 3 files filestem.names filestem.data filestem.test\&+` &` ` +`                    "File Format: .names`   The file begins with a comma separated list of classes ending with a period, followed by a blank line E.g, >50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                    $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]` K  %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     A  & c4.5 Output `    The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` t  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c4  t  (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c4  )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS` R  5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd`e  Inductive Learning`   ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationlxBa`&ae`a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc      H                       =      *         Ripper`  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uct  Ripper`  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c&!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summary`  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesx`  Y References b   pZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. L&Vb!bb  M     ,       &  =  "   5   W  M rwww.UniSA.edu.au/~shizz/,b(f(Z       !0 Questions?!4 Z g  Hb  /Mr P,o}?(o  0 hA Equation Equation.30,Microsoft Equation 3.00iB Equation Equation.30,Microsoft Equation 3.00jF Equation Equation.30,Microsoft Equation 3.0r0G MOVVP Paint.Picture0MOVVP0 H Equation Equation.20,Microsoft Equation 2.0B.www.UniSA.edu.au/~shizz0+J Clip (MS_ClipArt_Gallery.20,Microsoft Clip Gallery03`J Clip (MS_ClipArt_Gallery.50,Microsoft Clip Gallery07dL Clip (MS_ClipArt_Gallery.50,Microsoft Clip Gallery0iM  VISIO Visio.Drawing.40VISIO 4 Drawing0jN Clip (MS_ClipArt_Gallery.20,Microsoft Clip Gallery0kO  VISIO Visio.Drawing.40VISIO 4 Drawingxdhttp://www.mkp.com/books_catalog/1-55860-240-2.asp|hftp://ftp.ics.uci.edu/pub/machine-learning-databases/ 0 DTimes New Roman5 |dv 0|( 0@DTahomaew Roman5 |dv 0|( 0@" DWingdingsRoman5 |dv 0|( 0@0D[SOgdingsRoman5 |dv 0|( 0@@DCourier Newman5 |dv 0|( 0@1PDSymbol Newman5 |dv 0|( 0@`DfNbol Newman5 |dv 0|( 0@1pDArial Unicode MS |dv 0|( 0XDWingdings 3e MS |dv 0|( 0@DArialngs 3e MS |dv 0|( 0@"DPMingLiU 3e MS |dv 0|( 0@DMingLiU 3e MS |dv 0|( 0@1DjwiԚLiU 3e MS |dv 0|( 0DwiSO_GB23123e MS |dv 0|( 0@1DHelvetica3e MS |dv 0|( 0DMonotype SortsS |dv 0|( 0DHelvetica Black |dv 0|( 0 a.  @n?" dd@  @@`` 66`(( )o{:)?* ),#),b&!- 2  +   +(92G87 +(9P2G87 ),B0,<PE{,,,E+d+ (9P2G +<dF(922GQI#5 Z 5 Z  1E/   8K'   $&,*0;<9:7B523"134%'()/U  " #-./1256789:;<,+*0)('&TiX !C-.#"!   ,G+    Z[o R$ w՝F22$oTp ˒2b$+*KFB 22$j9wxV422$G);1/bAl#22$I{Oo*p>2$2$2$22$`R yuy!.22$Azxw`E=b622$Πhk_F:y(2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2b$VD\idxm 2$2$2$2$2$2$2$2$2$2$2b$YJT5(7D2$2$2$2$2$2b$LMa) ?{:2b$s8Pr%=* A2b$'*B&7s J2b$ʩ2R$Յ&%u+J8B%V<2R$j %$#=2R$b֌3K2R$Xeq|X"#Q2R$# G 2R$N^Ӵa qM.ZJ 2R$Eu3:5LJ$VMH 22$rzUZ2 2 0e0e A@A5%8c8c     ?1d0u0@Ty2 NP'p<'p@A)BCD|E? ff3f( @8 g4BdBdv 0pm`ppp@ <4BdBd` 0,6 g4&d&dv 0pp8 ph<4!d!d` 0,6 \ ʚ;ʚ;<4dddd{ 00___PPT10 z___PPT9\/ 0@(?^+ JChap4 Inductive Learning Zhongzhi ShiO =LAdvanced Computing Seminar Data Mining and Its Industrial Applications  Chapter 4  Inductive Learninghl ,b,bbf6f P Zhongzhi Shi, Markus Stumptner, Yalei Hao, Gerald Quirchmayr Knowledge and Software Engineering Lab Advanced Computing Research Centre School of Computer and Information Science University of South Australia )L>n<>bBb                !0>Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm Summary8fx ddX` f sBasic Concepts`  ~Data: Store on any media with certain format Information: Assign meaning to concrete data knowledge: Refine from informationlxf$*b$ f$#b$ f$b$  o-"Data Mining vs Knowledge Discovery##b &    IData mining Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. nnn d`b $/d`$' q  .Data Mining: A KDD Process(b b   ^Data mining core of knowledge discovery processJ0Z bb#bb 0 lData Warehouse Process`  m Macro Picture`  nDetailed picture`  *Knowledge Representation`  IProduction system Frame Semantic networks First order logic Ontology &FxxJ` J /Production System`  Rules IF (conditions) Then (conclusions) If ( animal has wing) and (animal can fly) Then (animal is a bird) 2xzxx`  e Production Systemd&     f Frame Structure0de&    g Semantic Networks0de&     0First Order Logic`  Student(John) Teacher(Markus) Father(x,y) Father(y,z) Grandfather(x,z):-Father(x,y),Father(y,z) If ( animal has wing) and (animal can fly) Then (animal is a bird) ({x6xb  hOntology `  @ Semantic Web: Ontology OWL Ontology schema Description Logic&x0xA` A 2Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryHfx dddG` f 1The Essence of Learning`  aLearning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time. [Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic {:algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   N    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  !              %Example of the Ordering of Hypotheses&&` & `  ~Version Space Search `   Version Space Example`  `  Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  9     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  )  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`   Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h```g    >     5             Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9         )     Entropy and Information Gain`    The ID3 Algorithm $a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; 8ZZc b   pThe ID3 Algorithm $a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc j  I     .     3  "          *         qC4.5 `   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan, some at specialized sites such as the University of California at Irvine. $` a T    "   !0!0srC4.5 `    C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc c b b 2b b b b b b  b b b b 5b b 6b b *b b $b c        2           5  6  *  $  ! Running c4.5 `   On cunix.columbia.edu ~amr2104/c4.5/bin/c4.5  u  f filestem c4.5 expects to find 3 files filestem.names filestem.data filestem.test\&+` &` ` +`                "File Format: .names`   The file begins with a comma separated list of classes ending with a period, followed by a blank line E.g, >50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                    $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]` K  %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     A  & c4.5 Output `    The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` t  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c4  t  (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c4  )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS` R  5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd`e  Inductive Learning`   ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationlxBa`&ae`a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc      H                       =      +         Ripper`  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uct  Ripper`  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c4!    6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdde  Summary`  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesx`  Y References b    pZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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l?ʴJ+U) /UArOE˸Y( z j#:H@t^ZnC$R1ҧe6Kpv!G6W2$2$2$22$`R yuy!.22$Azxw`E=b622$Πhk_F:y(2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2$2b$VD\idxm 2$2$2$2$2$2$2$2$2$2$2b$YJT5(7D2$2$2$2$2$2b$LMa) ?{:2b$s8Pr%=* A2b$'*B&7s J2b$ʩ2R$Յ&%u+J8B%V<2R$j %$#=2R$b֌3K2R$Xeq|X"#Q2R$# G 2R$N^Ӵa qM.ZJ 2R$Eu3:5LJ$VMH 22$rzUZ2 2 0e0e A@A5%8c8c     ?1d0u0@Ty2 NP'p<'p@A)BCD|E? ff3f( @8 g4BdBdv 0pm`ppp@ <4BdBd` 0,6 g4&d&dv 0pp8 ph<4!d!d` 0,6 \ ʚ;ʚ;<4dddd{ 00___PPT10 z___PPT9\/ 0@(?^+ JChap4 Inductive Learning Zhongzhi ShiO =LAdvanced Computing Seminar Data Mining and Its Industrial Applications  Chapter 4  Inductive Learninghl ,b,bbf6f P Zhongzhi Shi, Markus Stumptner, Yalei Hao, Gerald Quirchmayr Knowledge and Software Engineering Lab Advanced Computing Research Centre School of Computer and Information Science University of South Australia )L>n<>bBb                !0>Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm Summary8fx ddX` f sBasic Concepts`  ~Data: Store on any media with certain format Information: Assign meaning to concrete data knowledge: Refine from informationlxf$*b$ f$#b$ f$b$  o-"Data Mining vs Knowledge Discovery##b &    IData mining Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. nnn d`b $/d`$' q  .Data Mining: A KDD Process(b b   ^Data mining core of knowledge discovery processJ0Z bb#bb 0 lData Warehouse Process`  m Macro Picture`  nDetailed picture`  *Knowledge Representation`  IProduction system Frame Semantic networks First order logic Ontology &FxxJ` J /Production System`  Rules IF (conditions) Then (conclusions) If ( animal has wing) and (animal can fly) Then (animal is a bird) 2xzxx`  e Production Systemd&     f Frame Structure0de&    g Semantic Networks0de&     0First Order Logic`  Student(John) Teacher(Markus) Father(x,y) Father(y,z) Grandfather(x,z):-Father(x,y),Father(y,z) If ( animal has wing) and (animal can fly) Then (animal is a bird) ({x6xb  hOntology `  @ Semantic Web: Ontology OWL Ontology schema Description Logic&x0xA` A 2Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryHfx dddG` f 1The Essence of Learning`  aLearning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time. [Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   N    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  !              %Example of the Ordering of Hypotheses&&` & `  ~Version Space Search `   Version Space Example`  `  Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  9     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  )  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`   Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h```g    >     5             Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9         )     Entropy and Information Gain`    The ID3 Algorithm $a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; 8ZZc b   pThe ID3 Algorithm $a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc j  I     .     3  "          *         qC4.5 `   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan, some at specialized sites such as the University of California at Irvine. $` a T    "   !0!0srC4.5 `    C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc c b b 2b b b b b b  b b b b 5b b 6b b *b b $b c        2           5  6  *  $  ! Running c4.5 `   On cunix.columbia.edu ~amr2104/c4.5/bin/c4.5  u  f filestem c4.5 expects to find 3 files filestem.names filestem.data filestem.test\&+` &` ` +`                "File Format: .names`   The file begins with a comma separated list of classes ending with a period, followed by a blank line E.g, >50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                    $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]` K  %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     A  & c4.5 Output `    The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` t  T   ),#),    'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c4  t  (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c4  )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS` R  5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd`e  Inductive Learning`   ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationlxBa`&ae`a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc      H                       =      +         Ripper`  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uct  Ripper`  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c4!    6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdde  Summary`  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesx`  Y References b    pZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datat%xxVx%`bbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   O    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  "             %Example of the Ordering of Hypotheses&&` & ` ~Version Space Search `   Version Space Example`   Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  7     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  *  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`   Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4v 0|( 0`DfNbol Newman5 |dv 0|( 0@1pDArial Unicode MS |dv 0|( 0"DWingdings 3e MS |dv 0|( 0DArialngs 3e MS |dv 0|( 0"De0}fԚngs 3e MS |dv 0|( 0DMingLiUs 3e MS |dv 0|( 01DjwiԚLiUs 3Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h```g    ?     5            Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9            '       Entropy and Information Gain`    The ID3 Algorithm &a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; :ZZc b   pThe ID3 Algorithm &a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc c  >  Y  M  0       qC4.5 `   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan. Quinlan,J.R is working at RULEQUEST RESEARCH company, See5/C5.0 has been designed to operate on large databases and incorporates innovations such as boosting. b|ZwZ` a `    n   x   #   i   0rC4.5 `    C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc c b  b  2b b b b  b  $b$  (b( ,b, 0b0 4b4 58b8 50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                 $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]`  L %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     B & c4.5 Output `   The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` L      'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c&  u (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c 5 )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS`  S 5Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd` f Inductive Learning`   ZRipper`  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationxxBa`&ae ` a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc^     D           =    +         Ripperb0  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uc  u Ripperb0  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c &!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summaryb0  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesxb$  Y References b   cZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. Quinlan,J.R.: C4.5: Programs for Machine Learning Morgan Kauffman, 1993 T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datat%xxVx%`bbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   O    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  "             %Example of the Ordering of Hypotheses&&` & ` ~Version Space Search `   Version Space Example`   Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  7     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  *  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`  Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h```g    ?     5            Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9            '       Entropy and Information Gain`    The ID3 Algorithm &a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical  66h(4 ) { attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; :ZZc b   pThe ID3 Algorithm &a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc c  >  Y  M  0       qC4.5 `   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan. Quinlan,J.R is working at RULEQUEST RESEARCH company, See5/C5.0 has been designed to operate on large databases and incorporates innovations such as boosting. N|ZwZ` a ` n   x   #   i   0rC4.5 `    C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc c b  b  2b b b b  b  $b$  (b( ,b, 0b0 4b4 58b8 50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                 $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]`  L %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     B & c4.5 Output `   The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` L      'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c&  u (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c 5 )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS`  S 5Outlineb0  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd` f Inductive Learningb0   ZRipperb0  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationxxBa`&ae ` a`  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc^     D           =    +         Ripperb0  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uc  u Ripperb0  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c &!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summaryb0  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesxb$  Y References b   cZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. Quinlan,J.R.: C4.5: Programs for Machine Learning Morgan Kauffman, 1993 T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb`b tThe Essence of Learning` The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x` uThe Essence of Learning` The environment = Information source Database Text Web pages Image Video Space datat%xxVx%`bbC| 7The Essence of Learning` FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs$ vParadigms for Machine Learning` `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.BxxxxabF:   O    wParadigms for Machine Learning` The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb 8The Essence of Learning` jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T 9The Essence of Learning` The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``  On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb Concept Description` hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjbFA      :Attribute Types` Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb  Attribute Types` Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb  Attribute Types` Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb Z Attribute Types` 7Structured attribute For examples: Tree structure <xx " x8b 8 Inductive Learning` 4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx` zInductive Learning $a`   Inductive Learning $a`  {Inductive Learning Method $a`  Inductive Learning Method $a`  Inductive Learning Method $a`  Inductive Learning Method $a`  }Best-Hypothesis `  3Outline` eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0`f |Hypothesis Space`  !Training Examples for Enjoy Sport""`" \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb8 4    )is more_general_than_or_equal_to relation**`* Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equ                          ! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | ~  al_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb ;      '  "             %Example of the Ordering of Hypotheses&&`& ` ~Version Space Search `  Version Space Example`  Representing Version Space` The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bbV Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**`*  b Example Trace (1)0`b`  b Example Trace (2)$`b  ` Example Trace (3)$`b  ` Example Trace (4)$`b  ` How to Classify new Instances?` New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```F    2  New Instances to be Classified` A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbbF  7     3Remarks on Version Space and Candidate-Elimination44`4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`bFT  *  ( Drawbacks of Version Space `  Version-Space Merging`  Version-Space Merging`  IVSM Examples `  any-shape   b  IVSM Examples ` Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b6                                                                        Bias `  Biasa   Bias Selection & Evaluation `  Multi-Tiered Bias System `  Multi-Tiered Bias Search Space`  4Outline` eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd`f Decision Tree Learning`  Play tennis: Training examples` Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb CLS learning algorithm`  CLS learning algorithm`   ID3 Approach `    Decision Tree Representation` Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`  b 'Appropriate problems for decision Trees((`( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B` ?Learning of Decision Trees Top-Down Induction of Decision Trees@@`@ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h``` g    ?     5            Entropy` S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb Entropy` The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bbF#  N =  !Information Gain Search Heuristic""`" Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bbFt Q 7   Play Tennis: Information Gain` |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9            '       Entropy and Information Gain`   The ID3 Algorithm $a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; 8ZZc b   pThe ID3 Algorithm $a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc c  >  Y  M  0       qC4.5 `  c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan. Quinlan,J.R is working at RULEQUEST RESEARCH company, See5/C5.0 has been designed to operate on large databases and incorporates innovations such as boosting. D|ZwZ` a `~   x   #   i  !0rC4.5 `   C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc b b 2 b  b b b b  b   $b$ (b( ,b, 0b0 54b4 88b8 *50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`  #Example: census.names`  >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                 $File Format: .data, .test`  Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]` L %Example: adult.test`  25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     B & c4.5 Output `   The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` T      'Example output`  +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c*  u (Example output`  4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c5 )k-fold Cross Validation`  RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS` S 5Outlineb0 eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd`f Inductive Learningb0  ZRipperb0 Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationvxBa`&ae` a ` Ripper` separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c Ripper` procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLcccccccccz     D           =    +         Ripperb0 tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uc u Ripperb0 TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c *!   6Outline` eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPddf Summaryb0 Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesxb$ Y References b   cZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. Quinlan,J.R.: C4.5: Programs for Machine Learning Morgan Kauffman, 1993 T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. RP&PPVb!bb4V        ,  >  &  =  "   5      =  rwww.fzcyps.com/shizz/,b(f(  !0 Questions?!4 Z g  Hb  /M  u3 vnp`(  ` ` 3 rgֳgֳ ? ,    ` Tti gֳgֳ ? ` |hGiven: " Premise statements. Consists of facts, specific observations, intermediate generalizations that provide information about some objects, phenomena, processes, and so on. " Tentative inductive assertion. Provides a priori hypothesis held about the objects in the premise statement. " Background knowledge. Contains general and domain-specific concepts for interpreting the premises and inference rules relevant to the task of inference Find: Inductive assertion (hypothesis). It strongly or weakly implies the premise statements in the context of background knowledge and satisfies the preference criterion.0 n<Z50 l<Z0 n<Z0 n<Z0 n<Z cccc  H ` 0޽h ? 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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   O    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxx bb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  "              %Example of the Ordering of Hypotheses&&` & `  ~Version Space Search `   Version Space Example`   Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  9     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  *  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`  Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h```g    ?     5            Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9         )     Entropy and Information Gain`    The ID3 Algorithm $a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; 8ZZc b   pThe ID3 Algorithm $a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc c  >  Y  M  0       qC4.5 `   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan. Quinlan,J.R is working at RULEQUEST RESEARCH company, See5/C5.0 has been designed to operate on large databases and incorporates innovations such as boosting. D|ZwZ` a `T      i  !0rC4.5 `    C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc b b 2 b  b b b b  b   $b$ (b( ,b, 0b0 54b4 88b8 *50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                 $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]`  L %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     B & c4.5 Output `   The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` f  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c&  u (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c 5 )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS`  S 5Outlineb0  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd` f Inductive Learningb0   ZRipperb0  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationvxBa`&ae` a `  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc    H                       =      *         Ripperb0  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uc  u Ripperb0  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c &!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summaryb0  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesxb$  Y References b   cZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. Quinlan,J.R.: C4.5: Programs for Machine Learning Morgan Kauffman, 1993 T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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[Simon 1983] Machine learning is the study of how to make machines acquire new knowledge, new skills, and reorganize existing knowledge.bxb` b tThe Essence of Learning`  The environment supplies the source information to the learning system. The level and quality of the information will significantly affect the learning strategy.x`  uThe Essence of Learning`  The environment = Information source Database Text Web pages Image Video Space datav%xxVx%bbbC | 7The Essence of Learning`  FThe learning element uses this information to make improvements in an explicit knowledge base, and the performance element uses the knowledge base to perform its task. Inductive learning Analogical Learning Explanation Learning Genetic algorithm Neural networkFxvxbbs $ vParadigms for Machine Learning`  `The inductive paradigm The most widely studied method for symbolic learning is one of inducing a general concept description from a sequence of instances of the concept and known counterexamples of the concept. The task is to build a concept description from which all the previous positive instances can be rederived by universal instantiation but none of the previous negative instances can be rederived by the same process. The analogical paradigm Analogical reasoning is a strategy of inference that allows the transfer of knowledge from a known area into another area with similar properties.Bxxxxab@:   O    wParadigms for Machine Learning`  The analytic paradigm The methods attempt to formulate a generalization after analyzing few instances in terms of the systems's knowledge. Mainly deductive rather than inductive mechanisms are used for such learning. The genetic paradigm Genetic algorithms have been inspired by a direct analogy to mutations in biological reproduction and Darwinian natural selection. In principle, genetic algorithms encode a parallel search through concept space, with each process attempting coarse-grain hill climbing. The connectionist paradigm Connectionist learning systems, also called ``neural networks . Connectionist learning consists of readjusting weights in a fixed-topology network via specific learning algorithms xxxxxxxxbb  8The Essence of Learning`  jThe knowledge base contains predefined concepts, domain constrains heuristic rules and so on. Knowledge representation Knowledge consistence Knowledge redundancy 6_xWxb`T  9The Essence of Learning`  The performance element. The learning element is trying to improve the action of the performance element. The performance element applies knowledge to solve problems and evaluate the learning effects.2xi`b``   On Concept  `  The term ``concept" is an universal notion which reflects a general, abstract, and essential features. For example, ``triangle", ``animal", ``computer", all of them are concept. Horse, tiger, bird and so on are called as example of the concept ``animal". Concept contains two meanings, extension and intension. Intension. The set of attributes which reflect the essential features of a concept is called intension. Extension. The set of examples which satisfy the definition of a concept is called extension. Fruit StudentDExxx?xb  Concept Description`  hIn general, a concept can be described by the concept name, and list of the attributes and attribute-value pairs, that is, (Concept name (Attribute 1 Value1) (Attribute2 Value2) & (Attributen Valuen) In addition, concept description can be represented by first order logic. Each attribute is a predicate, concept name and attribute value can be viewed as arguments. Concept description is represented by predicate calculus|xxbjbj.bjbj/bb>bjbjb@A      :Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. Linear attribute Structured attribute *xxb   Attribute Types`  Nominal attribute is one that takes on a finite, unordered set of mutually exclusive values. For examples Color: red, green, blue Traffic: airline, railway, ship .jx; " xb   Attribute Types`  Linear attribute For examples Age: 1,2,& 100 Temperature: 20, 21,& Distance: 1km, 2km,& Rx " x  " x; " xZb  Z Attribute Types`  7Structured attribute For examples: Tree structure <xx " x8b  8 Inductive Learning`  4From particular examples to general conclusion, principle, rule apple eat tomato eat banana eat & & fruit eat (AxZx`  zInductive Learning $a`   Inductive Learning $a`   {Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   Inductive Learning Method $a`   }Best-Hypothesis `   3Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dddd0` f |Hypothesis Space`   !Training Examples for Enjoy Sport""` " \ Sky Temp Humidity Wind Water Forecast EnjoySport Sunny Warm Normal Strong Warm Same YES Sunny Warm High Strong Warm Same YES Rainy Cold High Strong Warm Change NO Sunny Warm High Strong Cool Change YES What is the general concept? `B F?bbb4 4    )is more_general_than_or_equal_to relation**` * Definition of more_general_than_or_equal_to relation: Let hj and hk be boolean-valued functions defined over X. Then hj is more_general_than_or_equal_to hk (hj g hk) iff ("xX) [(hk(x)=1)(hj(x)=1)] In our case the most general hypothesis - that every day is a positive example - is represented by ?, ?, ?, ?, ?, ?, and the most specific possible hypothesis - that no day is positive example - is represented by , , , , , .<6ZvZ0ZZZbb bbbjbbj*bbbbjb bbbjbbjbbbibbpbbbbbb;      '  "              %Example of the Ordering of Hypotheses&&` & `  ~Version Space Search `   Version Space Example`   Representing Version Space`  The General boundary, G, of version space VSH,E, is the set of its maximally general members The Specific boundary, S, of version space VSH,E, is the set of its maximally specific members Every member of the version space lies between these boundaries VSH,E, = {hH | ($sS) ($gG) (ghs)} where xy means x is more general or equal to y&] FZbbbbb2ob bb V Candidate-elimination algorithm `   Candidate-elimination algorithm `   )Converging Boundaries of the G and S sets**` *  b Example Trace (1)0`b`   b Example Trace (2)$`b   ` Example Trace (3)$`b   ` Example Trace (4)$`b   ` How to Classify new Instances?`  New instance i is classified as a positive instance if every hypothesis in the current version space classifies it as positive. Efficient test - iff the instance satisfies every member of S New instance i is classified as a negative instance if every hypothesis in the current version space classifies it as negative. Efficient test - iff the instance satisfies none of the members of G > dF ``r`<``` ``r`C```@    2  New Instances to be Classified`  A Sunny, Warm, Normal, Strong, Cool, Change (YES) B Rainy, Cold, Normal, Light, Warm, Same (NO) C Sunny, Warm, Normal, Light, Warm, Same (Ppos(C)=3/6) D Sunny, Cold, Normal, Strong, Warm, Same (Ppos(C)=2/6) b8 Fb+b b(b b(bbj b)bbjbb@  9     3Remarks on Version Space and Candidate-Elimination44` 4 The algorithm outputs a set of all hypotheses consistent with the training examples iff there are no errors in the training data iff there is some hypothesis in H that correctly describes the target concept The target concept is exactly learned when the S and G boundary sets converge to a single identical hypothesis. Applications learning regularities in chemical mass spectroscopy learning control rules for heuristic searchTZ{0Z}0Z2`0ZTbMbb-b/bbbbGb`b@T  *  ( Drawbacks of Version Space `   Version-Space Merging`   Version-Space Merging`   IVSM Examples `   any-shape   b  IVSM Examples `  Example Instance S Instance G Resulting S Resulting G [S,C] [S,C] [,] [S,C] [,] X [S,Sp] f [L,?] [?,Po] [S,C] [?,Po] X [L,O] f [S,?] [?,C] [S,C] [?,C] [S,Py] [?,Py] [?,C] [S,P] [S,P] [,] [S,C] [S,Po] [S,Py]J6 d d(d d<d d)dc  c   c  c   c  c   c  c   c  bbfbc bc bc bc bcbc bc*b`6                                                                        Bias `   Biasa   Bias Selection & Evaluation `   Multi-Tiered Bias System `   Multi-Tiered Bias Search Space`   4Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd(dd` f Decision Tree Learning`  Play tennis: Training examples`  Day Outlook Temperature Humidity Wind Play Tennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No,MZ0Zb  CLS learning algorithm`   CLS learning algorithm`    ID3 Approach `    Decision Tree Representation`  Representation: Internal node test on some property (attribute) Branch corresponds to attribute value Leaf node assigns a classification Decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances (Outlook = Sunny Humidity = Normal) (Outlook = Overcast) (Outlook = Rain Wind = Weak) y k d&(28(  2byblbbbbbbbbbbbbbbbbbbbbbbbbbR   Decision Tree Example`   b 'Appropriate problems for decision Trees((` ( Instances are represented by attribute-value pairs Target function has discrete output values Disjunctive hypothesis may be required Possibly noisy training data data may contain errors data may contain missing attribute values2 (B `B`  ?Learning of Decision Trees Top-Down Induction of Decision Trees@@` @ Algorithm: The ID3 learning algorithm (Quinlan, 1986) If all examples from E belong to the same class Cj then label the leaf with Cj else select the  best decision attribute A with values v1, v2, & , vn for next node divide the training set S into S1, & , Sn according to values v1,& ,vn recursively build subtrees T1, & , Tn for S1, & , Sn generate decision tree T6Z4Z"0Z90Z0ZEZ30Z 0Z ``$`````h```h`% `  `  `  `  `  `  h  `  `  h  `  `  h & `  `  `  `  h  `  `  h  `  `  h  `  `  ` ``h``h``h``h```g    ?     5            Entropy`  S - a sample of training examples; p+ (p-) is a proportion of positive (negative) examples in S Entropy(S) = expected number of bits needed to encode the classification of an arbitrary member of S Information theory: optimal length code assigns -log2 p bits to message having probability p Expected number of bits to encode  + or  - of random member of S: Entropy(S) - p- log2 p- - p+ log2 p+ Generally for c different classes Entropy(S) c- pi log2 pilaZf0Z(0Z/ZD0Z2/Z"0Z2 Zb#bbjbbj5b bbbZbb8bjb$bbBbbbbbbbbbjbbjbjbjbbjbjbbbb bbbbbbbjbbjbjb  Entropy`  The entropy function relative to a boolean classification, as the proportion of positive examples varies between 0 and 1 entropy as a measure of impurity in a collection of examples6Zxb=bb>#  N =  !Information Gain Search Heuristic""` " Gain(S,A) - the expected reduction in entropy caused by partitioning the examples of S according to the attribute A. a measure of the effectiveness of an attribute in classifying the training data Values(A) - possible values of the attribute A Sv - subset of S, for which attribute A has value v The best attribute has maximal Gain(S,A) Aim is to minimise the number of tests needed for class. uZQZZ/0Z4Z)0Z29ZZbbbbbMbbbbbbPbbbbbb%bbb b  b  b  b  b  b  b  b  b  b  b  b  b  b  b 9bb>t Q 7   Play Tennis: Information Gain`  |Values(Wind) = {Weak, Strong} S = [9+, 5-], E(S) = 0.940 Sweak = [6+, 2-], E(Sweak) = 0.811 Sstrong = [3+, 3-], E(Sstrong) = 1.0 Gain(S,Wind) = E(S) - (8/14) E(Sweak) - (6/14) E(Sstrong) = 0.940 - (8/14) 0.811 - (6/14) 1.0 = 0.048 Gain(S,Outlook) = 0.246 Gain(S,Humidity) = 0.151 Gain(S,Temperature) = 0.029ZcZA0ZP00Z M0Z2bbbbbbbbb bbbb bbj bbbbj bbj bbbbjbbbbbbbbbb bbbbj bbbbjb    9         )     Entropy and Information Gain`    The ID3 Algorithm $a `   function ID3 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set) returns a decision tree; begin If S is empty, return a single node with value Failure; If S consists of records all with the same value for the categorical attribute, return a single node with that value; If R is empty, then return a single node with as value the most frequent of the values of the categorical attribute that are found in records of S; [note that then there will be errors, that is, records that will be improperly classified]; 8ZZc b   pThe ID3 Algorithm $a `   Q Let D be the attribute with largest Gain(D,S) among attributes in R; Let {dj| j=1,2, .., m} be the values of attribute D; Let {Sj| j=1,2, .., m} be the subsets of S consisting respectively of records with value dj for attribute D; Return a tree with root labeled D and arcs labeled d1, d2, .., dm going respectively to the trees ID3(R-{D}, C, S1), ID3(R-{D}, C, S2), .., ID3(R-{D}, C, Sm); end ID3; *QZZRc c  >  Y  M  0       qC4.5 `   c4.5 is a program that creates a decision tree based on a set of labeled input data. This decision tree can then be tested against unseen labeled test data to quantify how well it generalizes. The software for C4.5 can be obtained with Quinlan's book. A wide variety of training and test data is available, some provided by Quinlan. Quinlan,J.R is working at RULEQUEST RESEARCH company, See5/C5.0 has been designed to operate on large databases and incorporates innovations such as boosting. D|ZwZ` a `T      i  !0rC4.5 `    C4.5 is a software extension of the basic ID3 algorithm designed by Quinlan to address the following issues not dealt with by ID3: Avoiding overfitting the data Determining how deeply to grow a decision tree. Reduced error pruning. Rule post-pruning. Handling continuous attributes. e.g., temperature Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs. Improving computational efficiency. ZZ3qZMZqZZc b b 2 b  b b b b  b   $b$ (b( ,b, 0b0 54b4 88b8 *50K, <=50K. The remaining lines have the following format (note the end of line period): Attribute: {ignore, discrete n, continuous, list}.\fM3f` ` M` 3`   #Example: census.names`   >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, etc. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, etc. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, etc. occupation: Tech-support, Craft-repair, Other-service, Sales, etc. relationship: Wife, Own-child, Husband, Not-in-family, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, etc.Pc                 $File Format: .data, .test`   Each line in these data files is a comma separated list of attribute values ending with a class label followed by a period. The attributes must be in the same order as described in the .names file. Unavailable values can be entered as  ? When creating test sets, make sure that you remove these data points from the training data.B|s]|` s` ]`  L %Example: adult.test`   25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K. 38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K. 28, Local-gov, 336951, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K. 44, Private, 160323, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 7688, 0, 40, United-States, >50K. 18, ?, 103497, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 30, United-States, <=50K. 34, Private, 198693, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K. 29, ?, 227026, HS-grad, 9, Never-married, ?, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K. 63, Self-emp-not-inc, 104626, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 32, United-States, >50K. 24, Private, 369667, Some-college, 10, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K. 55, Private, 104996, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K. 65, Private, 184454, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 6418, 0, 40, United-States, >50K. 36, Federal-gov, 212465, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K. "Pg 4  b  Z        f      +    v    F  !     B & c4.5 Output `   The decision tree proper. (weighted training examples/weighted training error) Tables of training error and testing error Confusion matrix You ll want to pipe the output of c4.5 to a text file for later viewing. E.g., c4.5  u  f filestem > filestem.results t5-` 5` ` -` ` f  T       'Example output`   +capital-gain > 6849 : >50K (203.0/6.2) | capital-gain <= 6849 : | | capital-gain > 6514 : <=50K (7.0/1.3) | | capital-gain <= 6514 : | | | marital-status = Married-civ-spouse: >50K (18.0/1.3) | | | marital-status = Divorced: <=50K (2.0/1.0) | | | marital-status = Never-married: >50K (0.0) | | | marital-status = Separated: >50K (0.0) | | | marital-status = Widowed: >50K (0.0) | | | marital-status = Married-spouse-absent: >50K (0.0) | | | marital-status = Married-AF-spouse: >50K (0.0) Tree saved ,P,c&  u (Example output`   4Evaluation on training data (4660 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 366( 7.9%) 92 659(14.1%) (16.0%) << Evaluation on test data (2376 items): Before Pruning After Pruning ---------------- --------------------------- Size Errors Size Errors Estimate 1692 421(17.7%) 92 354(14.9%) (16.0%) << (a) (b) <-classified as ---- ---- 328 251 (a): class >50K 103 1694 (b): class <=50K 5P5c 5 )k-fold Cross Validation`   RStart with one large data set. Using a script, randomly divide this data set into k sets. At each iteration, use k-1 sets to train the decision tree, and the remaining set to test the model. Repeat this k times and take the average testing error. The avg. error describes how well the learning algorithm can be applied to the data set.SS`  S 5Outlineb0  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryXfx dd?dd` f Inductive Learningb0   ZRipperb0  Ripper (Repeated Incremental Pruning to Producing Error Reduction) Ripper algorithm proposed by Cohen in 1995 Ripper is consisted of two phase: the first is to determine the initial rule set and the second is post-process rule optimizationvxBa`&ae` a `  Ripper`  separate-and-conquer rule learning algorithm. First the training data are divided into a growing set and a pruning set. Then this algorithm generates a rule set in a greedy fashion, a rule at a time. While generating a rule Ripper searches the most valuable rule for the current growing set in rule space which can be defined in the form of BNF. Immediately after a rule is extracted on growing set, it is pruned on pruning set. After pruning, the corresponding examples covered by that rule in the training set (growing and pruning sets) are deleted. The remaining training data are re-partitioned after each rule is learned in order to help stabilize any problems caused by a  bad-split . This process is repeated until the terminal conditions satisfy.(c  Ripper`  procedure Rule_Generating(Pos,Neg) begin Ruleset := {} while Pos {} do /* grow and prune a new rule */ split (Pos,Neg) into (GrowPos,GrowNeg) and (PrunePos,PruneNeg) Rule := GrowRule(GrowPos,GrowNeg) Rule := PruneRule(Rule,PrunePos,PruneNeg) if the terminal conditions satisfy then return Ruleset else add Rule to Ruleset remove examples covered by Rule from (Pos,Neg) endif endwhile return Ruleset endZ x#cccccc ccccLccccccccc    H                       =      *         Ripperb0  tAfter each rule is added into the rule set, the total description length, an integer value, of the rule set is computed. The description length gives a measure of the complexity and accuracy of a rule set. The terminal conditions satisfy when there are no positive examples left or the description length of the current rule set is more than the user-specified threshold. u(uc  u Ripperb0  TPost-process rule optimization Ripper uses some post-pruning techniques to optimize the rule set. This optimization is processed on the possible remaining positive examples. Re-optimizing the resultant rule set is called RIPPER2, and the general case of re-optimizing  k times is called RIPPERk. +(+c &!   6Outline`  eIntroduction Machine learning Version space and bias Decision tree learning Ripper algorithm SummaryLfx ddPdd f Summaryb0  Inductive Learning is an important approach for data mining Version space can be used to explain generalization and specialization ID 3 and C4.5 Ripper algorithms generate efficient rulesxb$  Y References b   cZhongzhi Shi. Principles of Machine Learning. International Academic Publishers, 1992 Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques Morgsn Kaufmann Publishers, 2000 Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press. 2002 H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. Quinlan,J.R.: C4.5: Programs for Machine Learning Morgan Kauffman, 1993 T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. 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" # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | ~   _HA shishiiSA՜.+,՜.+,D՜.+,0     ( k  Data Mining ĻʾngS.F.U.lA+j Times New RomanTahoma Wingdings Courier NewSymbolArial Unicode MS Wingdings 3Arial ¼wMingLiU˿w _GB2312 HelveticaMonotype SortsHelvetica BlackBlendsMicrosoft Equation 3.0 λͼͼMicrosoft Equation 2.0Microsoft Clip GalleryVISIO 4 DrawingnAdvanced Computing Seminar Data Mining and Its Industrial Applications Chapter 4 Inductive LearningOutlineBasic ConceptsPowerPoint ʾĸ#Data Mining vs Knowledge DiscoveryData Mini W { ng: A KDD ProcessData Warehouse ProcessMacro PictureDetailed pictureKnowledge RepresentationProduction System Production System Frame Structure Semantic NetworksFirst Order Logic OntologyOutlineThe Essence of LearningThe Essence of LearningThe Essence of LearningThe Essence of LearningParadigms for Machine LearningParadigms for Machine LearningThe Essence of LearningThe Essence of Learning On ConceptConcept DescriptionAttribute TypesAttribute TypesAttribute TypesAttribute TypesInductive LearningInductive Learning Inductive Learning Inductive Learning Method Inductive Learning Method Inductive Learning Method Inductive Learning Method Best-Hypothesis OutlineHypothesis Space"Training Examples for Enjoy Sport*is more_general_than_or_equal_to relation&Example of the Ordering of HypothesesVersion Space Search Version Space ExampleRepresenting Version Space Candidate-elimination algorithm Candidate-elimination algorithm*Converging Boundaries of the G and S setsExample Trace (1)Example Trace (2)Example Trace (3)Example Trace (4)How to Classify new Instances?New Instances to be Classified4Remarks on Version Space and Candidate-EliminationDrawbacks of Version Space Version-Space MergingVersion-Space MergingIVSM Examples IVSM Examples Bias BiasBias Selection & Evaluation Multi-Tiered Bias System Multi-Tiered Bias Search SpaceOutlineDecision Tree LearningPlay tennis: Training examplesCLS learning algorithmCLS learning algorithm ID3 ApproachDecision Tree RepresentationDecision Tree Example(Appropriate problems for decision Trees@Learning of Decision Trees Top-Down Induction of Decision TreesEntropyEntropy"Information Gain Search HeuristicPlay Tennis: Information GainEntropy and Information GainThe ID3 Algorithm The ID3 Algorithm C4.5 C4.5 Running c4.5File Format: .namesExample: census.namesFile Format: .data, .testExample: adult.test c4.5 OutputExample outputExample outputk-fold Cross ValidationOutlineInductive LearningRipperRipperRipperRipperRipperOutlineSummary Referenceswww.fzcyps.com/shizz/  õʾĸģǶ OLE  õƬjX 8@ _PID_HLINKSA 3http://www.mkp.com/books_catalog/1-55860-240-2.asphttp://www.intsci.ac.cn/shizzz