Description
Taylor & Francis Ltd Ensemble Methods Foundations And Algorithms 2012 Edition by Zhi-Hua Zhou
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field.After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity.Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement. IntroductionBasic Concepts Popular Learning AlgorithmsEvaluation and Comparison Ensemble Methods Applications of Ensemble MethodsBoostingA General Boosting Procedure The AdaBoost Algorithm Illustrative Examples Theoretical IssuesMulticlass Extension Noise ToleranceBaggingTwo Ensemble Paradigms The Bagging Algorithm Illustrative Examples Theoretical Issues Random Tree EnsemblesCombination MethodsBenefits of Combination AveragingVotingCombining by Learning Other Combination Methods Relevant MethodsDiversityEnsemble Diversity Error DecompositionDiversity Measures Information Theoretic DiversityDiversity GenerationEnsemble PruningWhat Is Ensemble Pruning Many Could Be Better Than All Categorization of Pruning Methods Ordering-Based Pruning Clustering-Based Pruning Optimization-Based PruningClustering EnsemblesClusteringCategorization of Clustering Ensemble Methods Similarity-Based Methods Graph-Based Methods Relabeling-Based Methods Transformation-Based MethodsAdvanced TopicsSemi-Supervised Learning Active Learning Cost-Sensitive Learning Class-Imbalance Learning Improving Comprehensibility Future Directions of EnsemblesReferencesIndexFurther Readings appear at the end of each chapter.