Pattern Classification Using Ensemble Methods

By: Lior Rokach

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Hardcover | 2 December 2009

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Hardcover
242 Pages

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22.86 x 15.24 x 1.42

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Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.

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You Can Find This Book In

Non-Fiction Mathematics Mathematical Foundation Set Theory Computing & I.T.Computer Science Artificial Intelligence Computer Vision Pattern Recognition Databases Data Mining Probability & Statistics

Booktopia Publisher Services World Scientific Publishing

Preface	p. vii
Introduction to Pattern Classification	p. 1
Pattern Classification	p. 2
Induction Algorithms	p. 4
Rule Induction	p. 5
Decision Trees	p. 5
Bayesian Methods	p. 8
Overview	p. 8
Naïve Bayes	p. 9
The Basic Naïve Bayes Classifier	p. 9
Naïve Bayes Induction for Numeric Attributes	p. 12
Correction to the Probability Estimation	p. 12
Laplace Correction	p. 13
No Match	p. 14
Other Bayesian Methods	p. 14
Other Induction Methods	p. 14
Neural Networks	p. 14
Genetic Algorithms	p. 17
Instance-based Learning	p. 17
Support Vector Machines	p. 18
Introduction to Ensemble Learning	p. 19
Back to the Roots	p. 20
The Wisdom of Crowds	p. 22
The Bagging Algorithm	p. 22
The Boosting Algorithm	p. 28
The AdaBoost Algorithm	p. 28
No Free Lunch Theorem and Ensemble Learning	p. 36
Bias-Variance Decomposition and Ensemble Learning	p. 38
Occam's Razor and Ensemble Learning	p. 40
Classifier Dependency	p. 41
Dependent Methods	p. 42
Model-guided Instance Selection	p. 42
Basic Boosting Algorithms	p. 42
Advanced Boosting Algorithms	p. 44
Incremental Batch Learning	p. 51
Independent Methods	p. 51
Bagging	p. 53
Wagging	p. 54
Random Forest and Random Subspace Projection	p. 55
Non-Linear Boosting Projection (NLBP)	p. 56
Cross-validated Committees	p. 58
Robust Boosting	p. 59
Ensemble Methods for Advanced Classification Tasks	p. 61
Cost-Sensitive Classification	p. 61
Ensemble for Learning Concept Drift	p. 63
Reject Driven Classification	p. 63
Ensemble Classification	p. 65
Fusions Methods	p. 65
Weighting Methods	p. 65
Majority Voting	p. 66
Performance Weighting	p. 67
Distribution Summation	p. 68
Bayesian Combination	p. 68
Dempster-Shafer	p. 69
Vogging	p. 69
Naïve Bayes	p. 69
Entropy Weighting	p. 70
Density-based Weighting	p. 70
DEA Weighting Method	p. 70
Logarithmic Opinion Pool	p. 71
Order Statistics	p. 71
Selecting Classification	p. 71
Partitioning the Instance Space	p. 74
The K-Means Algorithm as a Decomposition Tool	p. 75
Determining the Number of Subsets	p. 78
The Basic K-Classifier Algorithm	p. 78
The Heterogeneity Detecting K-Classifier (HDK-Classifier)	p. 81
Running-Time Complexity	p. 81
Mixture of Experts and Meta Learning	p. 82
Stacking	p. 82
Arbiter Trees	p. 85
Combiner Trees	p. 88
Grading	p. 88
Gating Network	p. 89
Ensemble Diversity	p. 93
Overview	p. 93
Manipulating the Inducer	p. 94
Manipulation of the Inducer's Parameters	p. 95
Starting Point in Hypothesis Space	p. 95
Hypothesis Space Traversal	p. 95
Manipulating the Training Samples	p. 96
Resampling	p. 96
Creation	p. 97
Partitioning	p. 100
Manipulating the Target Attribute Representation	p. 101
Label Switching	p. 102
Partitioning the Search Space	p. 103
Divide and Conquer	p. 104
Feature Subset-based Ensemble Methods	p. 105
Random-based Strategy	p. 106
Reduct-based Strategy	p. 106
Collective-Performance-based Strategy	p. 107
Feature Set Partitioning	p. 108
Rotation Forest	p. 111
Multi-Inducers	p. 112
Measuring the Diversity	p. 114
Ensemble Selection	p. 119
Ensemble Selection	p. 119
Pre Selection of the Ensemble Size	p. 120
Selection of the Ensemble Size While Training	p. 120
Pruning - Post Selection of the Ensemble Size	p. 121
Ranking-based	p. 122
Search based Methods	p. 123
Collective Agreement-based Ensemble Pruning Method	p. 124
Clustering-based Methods	p. 129
Pruning Timing	p. 129
Pre-combining Pruning	p. 129
Post-combining Pruning	p. 130
Error Correcting Output Codes	p. 133
Code-matrix Decomposition of Multiclass Problems	p. 135
Type I - Training an Ensemble Given a Code-Matrix	p. 136
Error correcting output codes	p. 138
Code-Matrix Framework	p. 139
Code-matrix Design Problem	p. 140
Orthogonal Arrays (OA)	p. 144
Hadamard Matrix	p. 146
Probabilistic Error Correcting Output Code	p. 146
Other ECOC Strategies	p. 147
Type II - Adapting Code-matrices to the Multiclass Problems	p. 149
Evaluating Ensembles of Classifiers	p. 153
Generalization Error	p. 153
Theoretical Estimation of Generalization Error	p. 154
Empirical Estimation of Generalization Error	p. 155
Alternatives to the Accuracy Measure	p. 157
The F-Measure	p. 158
Confusion Matrix	p. 160
Classifier Evaluation under Limited Resources	p. 161
ROC Curves	p. 163
Hit Rate Curve	p. 163
Qrecall (Quota Recall)	p. 164
Lift Curve	p. 164
Pearson Correlation Coefficient	p. 165
Area Under Curve (AUC)	p. 166
Average Hit Rate	p. 167
Average Qrecall	p. 168
Potential Extract Measure (PEM)	p. 170
Statistical Tests for Comparing Ensembles	p. 172
McNemar's Test	p. 173
A Test for the Difference of Two Proportions	p. 174
The Resampled Paired t Test	p. 175
The k-fold Cross-validated Paired t Test	p. 176
Computational Complexity	p. 176
Interpretability of the Resulting Ensemble	p. 177
Scalability to Large Datasets	p. 178
Robustness	p. 179
Stability	p. 180
Flexibility	p. 180
Usability	p. 180
Software Availability	p. 180
Which Ensemble Method Should be Used?	p. 181
Bibliography	p. 185
Index	p. 223
Table of Contents provided by Ingram. All Rights Reserved.

Pattern Classification Using Ensemble Methods

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