Multi-Objective Machine Learning

  • Yaochu Jin

Part of the Studies in Computational Intelligence book series (SCI, volume 16)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Multi-Objective Clustering, Feature Extraction and Feature Selection

    1. Front Matter
      Pages I-XIII
    2. Mohua Banerjee, Sushmita Mitra, Ashish Anand
      Pages 3-20
    3. Julia Handl, Joshua Knowles
      Pages 21-47
    4. Luiz S. Oliveira, Marisa Morita, Robert Sabourin
      Pages 49-74
    5. Yang Zhang, Peter I Rockett
      Pages 75-99
  3. Multi-Objective Learning for Accuracy Improvement

    1. Front Matter
      Pages I-XIII
    2. Tomonari Furukawa, Chen Jian Ken Lee, John G. Michopoulos
      Pages 125-149
    3. Antônio Pádua Braga, Ricardo H. C. Takahashi, Marcelo Azevedo Costa, Roselito de Albuquerque Teixeira
      Pages 151-171
    4. Thorsten Suttorp, Christian Igel
      Pages 199-220
    5. Ester Bernadó-Mansilla, Xavier Llorà, Ivan Traus
      Pages 261-288
  4. Multi-Objective Learning for Interpretability Improvement

    1. Front Matter
      Pages I-XIII
    2. Yaochu Jin, Bernhard Sendhoff, Edgar Körner
      Pages 291-312
    3. Urszula Markowska-Kaczmar, Krystyna Mularczyk
      Pages 313-338
    4. Hanli Wang, Sam Kwong, Yaochu Jin, Chi-Ho Tsang
      Pages 339-364

About this book

Introduction

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.

Keywords

Support Vector Machine decision tree evolution fuzzy fuzzy system fuzzy systems genetic algorithms intelligent systems learning machine learning model multi-objective optimization neural network neural networks optimization

Editors and affiliations

  • Yaochu Jin
    • 1
  1. 1.Honda Research Institute Europe GmbHOffenbachGermany

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-33019-4
  • Copyright Information Springer 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-30676-4
  • Online ISBN 978-3-540-33019-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book