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)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
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.
Similar content being viewed by others
Keywords
Table of contents (27 chapters)
-
Front Matter
-
Multi-Objective Clustering, Feature Extraction and Feature Selection
-
Front Matter
-
-
Multi-Objective Learning for Accuracy Improvement
-
Front Matter
-
-
Multi-Objective Learning for Interpretability Improvement
-
Front Matter
-
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