Ensemble Methods - Foundations and Algorithms

Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

List of contents

Preface Notations 1. Introduction 2. Boosting 3. Bagging 4. Combination Methods 5. Diversity 6. Ensemble Pruning 7. Clustering Ensemble 8. Anomaly Detection and Isolation Forest 9. Semi-Supervised Ensemble 10. Class-Imbalance and Cost-Sensitive Ensemble 11. Deep Learning and Deep Forest 12. Advanced Topics References Index

About the author

Zhi-Hua Zhou, Professor of Computer Science and Artificial Intelligence at Nanjing University, President of IJCAI trustee, Fellow of the ACM, AAAI, AAAS, IEEE, recipient of the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, CCF-ACM Artificial Intelligence Award.

Summary

Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

Product details

• Authors: Zhi-Hua Zhou, Zhou Zhi-Hua
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 27.02.2025
• EAN: 9781032960609
• ISBN: 978-1-0-3296060-9
• No. of pages: 348
• Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Data Mining, Artificial Intelligence, BUSINESS & ECONOMICS / Statistics, COMPUTERS / Programming / Games, COMPUTERS / Machine Theory, Probability & statistics, Automatic control engineering, Computer architecture & logic design, Probability and statistics, Computer architecture and logic design, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Data Analytics