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.
Coverage of Bayes decision theory and experimental comparison of classifiersEssential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among othersChapters on classifier selection, diversity, and ensemble feature selection
Preface xvAcknowledgements xxi1.Fundamentals of Pattern Recognition 12.Base Classifiers 493.An Overview of the Field 944.Combining Label Outputs 1115.Combining Continuous-Valued Outputs 1436.Ensemble Methods 1867.Classifier Selection 2308.Diversity in Classifier Ensembles 2479.Ensemble Feature Selection 29010. A Final Thought 326References 327Index 353
Ludmila Kuncheva :- 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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