1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning - post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I - training an ensemble given a code-matrix. 6.3. Type II - adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used?

This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.

This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced. Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized. The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.

This dissertation studies the use of multiple classifiers (ensembles or committees) in learning tasks. Both theoretical and practical aspects of combining classifiers are studied.