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E-raamat: Combining Pattern Classifiers: Methods and Algorithms

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(School of Informatics at the University of Wales, Bangor, UK)
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  • Ilmumisaeg: 13-Aug-2014
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118914540
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Combining Pattern Classifiers: Methods and AlgorithmsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 13-Aug-2014
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118914540
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A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods.

Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes:





Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection

With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.
Preface xv
Acknowledgements xxi
1 Fundamentals of Pattern Recognition
1(48)
1.1 Basic Concepts: Class, Feature, Data Set
1(8)
1.1.1 Classes and Class Labels
1(1)
1.1.2 Features
2(1)
1.1.3 Data Set
3(3)
1.1.4 Generate Your Own Data
6(3)
1.2 Classifier, Discriminant Functions, Classification Regions
9(2)
1.3 Classification Error and Classification Accuracy
11(8)
1.3.1 Where Does the Error Come From? Bias and Variance
11(2)
1.3.2 Estimation of the Error
13(1)
1.3.3 Confusion Matrices and Loss Matrices
14(1)
1.3.4 Training and Testing Protocols
15(2)
1.3.5 Overtraining and Peeking
17(2)
1.4 Experimental Comparison of Classifiers
19(11)
1.4.1 Two Trained Classifiers and a Fixed Testing Set
20(2)
1.4.2 Two Classifier Models and a Single Data Set
22(4)
1.4.3 Two Classifier Models and Multiple Data Sets
26(1)
1.4.4 Multiple Classifier Models and Multiple Data Sets
27(3)
1.5 Bayes Decision Theory
30(5)
1.5.1 Probabilistic Framework
30(1)
1.5.2 Discriminant Functions and Decision Boundaries
31(2)
1.5.3 Bayes Error
33(2)
1.6 Clustering and Feature Selection
35(5)
1.6.1 Clustering
35(2)
1.6.2 Feature Selection
37(3)
1.7 Challenges of Real-Life Data
40(9)
Appendix
41(1)
1.A.1 Data Generation
41(1)
1.A.2 Comparison of Classifiers
42(1)
1.A.2.1 MATLAB Functions for Comparing Classifiers
42(3)
1.A.2.2 Critical Values for Wilcoxon and Sign Test
45(2)
1.A.3 Feature Selection
47(2)
2 Base Classifiers
49(45)
2.1 Linear and Quadratic Classifiers
49(6)
2.1.1 Linear Discriminant Classifier
49(3)
2.1.2 Nearest Mean Classifier
52(1)
2.1.3 Quadratic Discriminant Classifier
52(1)
2.1.4 Stability of LDC and QDC
53(2)
2.2 Decision Tree Classifiers
55(11)
2.2.1 Basics and Terminology
55(2)
2.2.2 Training of Decision Tree Classifiers
57(1)
2.2.3 Selection of the Feature for a Node
58(2)
2.2.4 Stopping Criterion
60(3)
2.2.5 Pruning of the Decision Tree
63(1)
2.2.6 C4.5 and ID3
64(1)
2.2.7 Instability of Decision Trees
64(1)
2.2.8 Random Trees
65(1)
2.3 The Naive Bayes Classifier
66(2)
2.4 Neural Networks
68(5)
2.4.1 Neurons
68(2)
2.4.2 Rosenblatt's Perceptron
70(1)
2.4.3 Multi-Layer Perceptron
71(2)
2.5 Support Vector Machines
73(7)
2.5.1 Why Would It Work?
73(1)
2.5.2 Classification Margins
74(2)
2.5.3 Optimal Linear Boundary
76(2)
2.5.4 Parameters and Classification Boundaries of SVM
78(2)
2.6 The κ-Nearest Neighbor Classifier (A:-nn)
80(2)
2.7 Final Remarks
82(12)
2.7.1 Simple or Complex Models?
82(1)
2.7.2 The Triangle Diagram
83(2)
2.7.3 Choosing a Base Classifier for Ensembles
85(1)
Appendix
85(1)
2.A.1 MATLAB Code for the Fish Data
85(1)
2.A.2 MATLAB Code for Individual Classifiers
86(1)
2.A.2.1 Decision Tree
86(3)
2.A.2.2 Naive Bayes
89(1)
2.A.2.3 Multi-Layer Perceptron
90(2)
2.A.2.4 1-nn Classifier
92(2)
3 An Overview of the Field
94(17)
3.1 Philosophy
94(4)
3.2 Two Examples
98(2)
3.2.1 The Wisdom of the "Classifier Crowd"
98(1)
3.2.2 The Power of Divide-and-Conquer
98(2)
3.3 Structure of the Area
100(5)
3.3.1 Terminology
100(1)
3.3.2 A Taxonomy of Classifier Ensemble Methods
100(4)
3.3.3 Classifier Fusion and Classifier Selection
104(1)
3.4 Quo Vadis?
105(6)
3.4.1 Reinventing the Wheel?
105(1)
3.4.2 The Illusion of Progress?
106(1)
3.4.3 A Bibliometric Snapshot
107(4)
4 Combining Label Outputs
111(32)
4.1 Types of Classifier Outputs
111(1)
4.2 A Probabilistic Framework for Combining Label Outputs
112(1)
4.3 Majority Vote
113(12)
4.3.1 "Democracy" in Classifier Combination
113(1)
4.3.2 Accuracy of the Majority Vote
114(3)
4.3.3 Limits on the Majority Vote Accuracy: An Example
117(2)
4.3.4 Patterns of Success and Failure
119(5)
4.3.5 Optimality of the Majority Vote Combiner
124(1)
4.4 Weighted Majority Vote
125(3)
4.4.1 Two Examples
126(1)
4.4.2 Optimality of the Weighted Majority Vote Combiner
127(1)
4.5 Naive-Bayes Combiner
128(4)
4.5.1 Optimality of the Naive Bayes Combiner
128(2)
4.5.2 Implementation of the NB Combiner
130(2)
4.6 Multinomial Methods
132(3)
4.7 Comparison of Combination Methods for Label Outputs
135(8)
Appendix
137(1)
4.A.1 Matan's Proof for the Limits on the Majority Vote Accuracy
137(2)
4.A.2 Selected MATLAB Code
139(4)
5 Combining Continuous-Valued Outputs
143(43)
5.1 Decision Profile
143(1)
5.2 How Do We Get Probability Outputs?
144(6)
5.2.1 Probabilities Based on Discriminant Scores
144(3)
5.2.2 Probabilities Based on Counts: Laplace Estimator
147(3)
5.3 Nontrainable (Fixed) Combination Rules
150(16)
5.3.1 A Generic Formulation
150(2)
5.3.2 Equivalence of Simple Combination Rules
152(1)
5.3.3 Generalized Mean Combiner
153(3)
5.3.4 A Theoretical Comparison of Simple Combiners
156(4)
5.3.5 Where Do They Come From?
160(6)
5.4 The Weighted Average (Linear Combiner)
166(6)
5.4.1 Consensus Theory
166(1)
5.4.2 Added Error for the Weighted Mean Combination
167(1)
5.4.3 Linear Regression
168(4)
5.5 A Classifier as a Combiner
172(3)
5.5.1 The Supra Bayesian Approach
172(1)
5.5.2 Decision Templates
173(2)
5.5.3 A Linear Classifier
175(1)
5.6 An Example of Nine Combiners for Continuous-Valued Outputs
175(1)
5.7 To Train or Not to Train?
176(10)
Appendix
178(1)
5.A.1 Theoretical Classification Error for the Simple Combiners
178(1)
5.A.1.1 Set-up and Assumptions
178(2)
5.A.1.2 Individual Error
180(1)
5.A.1.3 Minimum and Maximum
180(1)
5.A.1.4 Average (Sum)
181(1)
5.A.1.5 Median and Majority Vote
182(1)
5.A.1.6 Oracle
183(1)
5.A.2 Selected MATLAB Code
183(3)
6 Ensemble Methods
186(44)
6.1 Bagging
186(4)
6.1.1 The Origins: Bagging Predictors
186(1)
6.1.2 Why Does Bagging Work?
187(2)
6.1.3 Out-of-bag Estimates
189(1)
6.1.4 Variants of Bagging
190(1)
6.2 Random Forests
190(2)
6.3 AdaBoost
192(11)
6.3.1 The AdaBoost Algorithm
192(2)
6.3.2 The arc-x4 Algorithm
194(1)
6.3.3 Why Does AdaBoost Work?
195(4)
6.3.4 Variants of Boosting
199(1)
6.3.5 A Famous Application: AdaBoost for Face Detection
199(4)
6.4 Random Subspace Ensembles
203(1)
6.5 Rotation Forest
204(4)
6.6 Random Linear Oracle
208(3)
6.7 Error Correcting Output Codes (ECOC)
211(19)
6.7.1 Code Designs
212(2)
6.7.2 Decoding
214(2)
6.7.3 Ensembles of Nested Dichotomies
216(2)
Appendix
218(1)
6.A.1 Bagging
218(2)
6.A.2 AdaBoost
220(3)
6.A.3 Random Subspace
223(2)
6.A.4 Rotation Forest
225(3)
6.A.5 Random Linear Oracle
228(1)
6.A.6 ECOC
229(1)
7 Classifier Selection
230(17)
7.1 Preliminaries
230(1)
7.2 Why Classifier Selection Works
231(2)
7.3 Estimating Local Competence Dynamically
233(6)
7.3.1 Decision-Independent Estimates
233(5)
7.3.2 Decision-Dependent Estimates
238(1)
7.4 Pre-Estimation of the Competence Regions
239(3)
7.4.1 Bespoke Classifiers
240(1)
7.4.2 Clustering and Selection
241(1)
7.5 Simultaneous Training of Regions and Classifiers
242(2)
7.6 Cascade Classifiers
244(3)
Appendix: Selected MATLAB Code
244(1)
7.A.1 Banana Data
244(1)
7.A.2 Evolutionary Algorithm for a Selection Ensemble for the Banana Data
245(2)
8 Diversity in Classifier Ensembles
247(43)
8.1 What Is Diversity?
247(3)
8.1.1 Diversity for a Point-Value Estimate
248(1)
8.1.2 Diversity in Software Engineering
248(1)
8.1.3 Statistical Measures of Relationship
249(1)
8.2 Measuring Diversity in Classifier Ensembles
250(6)
8.2.1 Pairwise Measures
250(1)
8.2.2 Nonpairwise Measures
251(5)
8.3 Relationship Between Diversity and Accuracy
256(14)
8.3.1 An Example
256(2)
8.3.2 Relationship Patterns
258(4)
8.3.3 A Caveat: Independent Outputs ≠ Independent Errors
262(3)
8.3.4 Independence Is Not the Best Scenario
265(2)
8.3.5 Diversity and Ensemble Margins
267(3)
8.4 Using Diversity
270(9)
8.4.1 Diversity for Finding Bounds and Theoretical Relationships
270(1)
8.4.2 Kappa-error Diagrams and Ensemble Maps
271(4)
8.4.3 Overproduce and Select
275(4)
8.5 Conclusions: Diversity of Diversity
279(11)
Appendix
280(1)
8.A.1 Derivation of Diversity Measures for Oracle Outputs
280(1)
8.A.1.1 Correlation ρ
280(1)
8.A.1.2 Interrater Agreement κ
281(1)
8.A.2 Diversity Measure Equivalence
282(2)
8.A.3 Independent Outputs ≠ Independent Errors
284(2)
8.A.4 A Bound on the Kappa-Error Diagram
286(1)
8.A.5 Calculation of the Pareto Frontier
287(3)
9 Ensemble Feature Selection
290(36)
9.1 Preliminaries
290(5)
9.1.1 Right and Wrong Protocols
290(4)
9.1.2 Ensemble Feature Selection Approaches
294(1)
9.1.3 Natural Grouping
294(1)
9.2 Ranking by Decision Tree Ensembles
295(4)
9.2.1 Simple Count and Split Criterion
295(2)
9.2.2 Permuted Features or the "Noised-up" Method
297(2)
9.3 Ensembles of Rankers
299(6)
9.3.1 The Approach
299(1)
9.3.2 Ranking Methods (Criteria)
300(5)
9.4 Random Feature Selection for the Ensemble
305(10)
9.4.1 Random Subspace Revisited
305(1)
9.4.2 Usability, Coverage, and Feature Diversity
306(6)
9.4.3 Genetic Algorithms
312(3)
9.5 Nonrandom Selection
315(2)
9.5.1 The "Favorite Class" Model
315(1)
9.5.2 The Iterative Model
315(1)
9.5.3 The Incremental Model
316(1)
9.6 A Stability Index
317(9)
9.6.1 Consistency Between a Pair of Subsets
317(2)
9.6.2 A Stability Index for K Sequences
319(1)
9.6.3 An Example of Applying the Stability Index
320(2)
Appendix
322(1)
9.A.1 MATLAB Code for the Numerical Example of Ensemble Ranking
322(1)
9.A.2 MATLAB GA Nuggets
322(2)
9.A.3 MATLAB Code for the Stability Index
324(2)
10 A Final Thought
326(1)
References 327(26)
Index 353
Ludmila Kuncheva is a Professor of Computer Science at Bangor University, United Kingdom. She has received two IEEE Best Paper awards. In 2012, Dr. Kuncheva was awarded a Fellowship to the International Association for Pattern Recognition (IAPR) for her contributions to multiple classifier systems.
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