Ensemble methods : foundations and algorithms

Introduction Basic Concepts Popular Learning Algorithms Evaluation and Comparison Ensemble Methods Applications of Ensemble Methods Boosting A General Boosting Procedure The AdaBoost Algorithm Illustrative Examples Theoretical Issues Multiclass Extension Noise Tolerance Bagging Two Ensemble Paradigms The Bagging Algorithm Illustrative Examples Theoretical Issues Random Tree Ensembles Combination Methods Benefits of Combination Averaging Voting Combining by Learning Other Combination Methods Relevant Methods Diversity Ensemble Diversity Error Decomposition Diversity Measures Information Theoretic Diversity Diversity Generation Ensemble Pruning What Is Ensemble Pruning Many Could Be Better Than All Categorization of Pruning Methods Ordering-Based Pruning Clustering-Based Pruning Optimization-Based Pruning Clustering Ensembles Clustering Categorization of Clustering Ensemble Methods Similarity-Based Methods Graph-Based Methods Relabeling-Based Methods Transformation-Based Methods Advanced Topics Semi-Supervised Learning Active Learning Cost-Sensitive Learning Class-Imbalance Learning Improving Comprehensibility Future Directions of Ensembles References Index Further Readings appear at the end of each chapter.