Ensemble Methods: Foundations and Algorithms

Zhi-Hua Zhou

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June 6, 2012 by Chapman and Hall/CRC
Professional - 236 Pages - 73 B/W Illustrations
ISBN 9781439830031 - CAT# K11467
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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Features

  • Supplies the basics for readers unfamiliar with machine learning and pattern recognition
  • Introduces the use of ensemble methods in computer vision, computer security, medical imaging, and famous data mining competitions, such as the KDD-Cup and Netflix Prize
  • Presents the theoretical foundations and extensions of many ensemble methods, including Boosting, Bagging, Random Trees, and Stacking
  • Covers nearly all aspects of ensemble techniques such as combination methods and diversity generation methods
  • Highlights future research directions
  • Provides additional reading sections in each chapter and references at the back of the book

Summary

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.