Ensemble Methods: Foundations and Algorithms, 1st Edition (Hardback) book cover

Ensemble Methods

Foundations and Algorithms, 1st Edition

By Zhi-Hua Zhou

Chapman and Hall/CRC

236 pages | 73 B/W Illus.

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pub: 2012-06-06
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Description

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.

Reviews

"… a valuable contribution to theoretical and practical ensemble learning. The material is very well presented, preliminaries and basic knowledge are discussed in detail, and many illustrations and pseudo-code tables help to understand the facts of this interesting field of research. Therefore, the book will become a helpful tool for practitioners working in the field of machine learning or pattern recognition as well as for students of engineering or computer sciences at the graduate and postgraduate level. I heartily recommend this book!"

IEEE Computational Intelligence Magazine, February 2013

"While the book is rather written for a machine learning and pattern recognition audience, the terminology is well explained and therefore also easily understandable for readers from other areas. In general the book is well structured and written and presents nicely the different ideas and approaches for combining single learners as well as their strengths and limitations."

—Klaus Nordhausen, International Statistical Review (2013), 81

"Professor Zhou’s book is a comprehensive introduction to ensemble methods in machine learning. It reviews the latest research in this exciting area. I learned a lot reading it!"

—Thomas G. Dietterich, Professor and Director of Intelligent Systems Research, Oregon State University, Corvallis, USA; ACM Fellow; and Founding President of the International Machine Learning Society

"This is a timely book. Right time and right book … with an authoritative but inclusive style that will allow many readers to gain knowledge on the topic."

—Fabio Roli, University of Cagliari, Italy

Table of Contents

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.

About the Author

Zhi-Hua Zhou is a professor in the Department of Computer Science and Technology and the National Key Laboratory for Novel Software Technology at Nanjing University. Dr. Zhou is the founding steering committee co-chair of ACML and associate editor-in-chief, associate editor, and editorial board member of numerous journals. He has published extensively in top-tier journals, chaired many conferences, and won six international journal/conference/competition awards. His research interests encompass the areas of machine learning, data mining, pattern recognition, and multimedia information retrieval.

About the Series

Chapman & Hall/CRC Machine Learning & Pattern Recognition

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM021030
COMPUTERS / Database Management / Data Mining
COM037000
COMPUTERS / Machine Theory