Overview
- Selected collection of recent research on multi-objective approach to machine learning
- Recent developments in evolutionary multi-objective optimization
- Applies the concept of Pareto-optimality to machine learning
Part of the book series: Studies in Computational Intelligence (SCI, volume 16)
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Table of contents (27 chapters)
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Front Matter
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Multi-Objective Clustering, Feature Extraction and Feature Selection
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Front Matter
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Multi-Objective Learning for Accuracy Improvement
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Front Matter
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Multi-Objective Learning for Interpretability Improvement
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Front Matter
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Keywords
About this book
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
Editors and Affiliations
Bibliographic Information
Book Title: Multi-Objective Machine Learning
Editors: Yaochu Jin
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/3-540-33019-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2006
Hardcover ISBN: 978-3-540-30676-4Published: 10 February 2006
Softcover ISBN: 978-3-642-06796-9Published: 22 November 2010
eBook ISBN: 978-3-540-33019-6Published: 10 June 2007
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 660
Number of Illustrations: 254 b/w illustrations
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Complex Systems, Statistical Physics and Dynamical Systems