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
  5. Multi-Objective Ensemble Generation

    1. Front Matter
      Pages I-XIII
    2. Arjun Chandra, Huanhuan Chen, Xin Yao
      Pages 429-464
    3. Hisao Ishibuchi, Yusuke Nojima
      Pages 507-530
  6. Applications of Multi-Objective Machine Learning

    1. Front Matter
      Pages I-XIII
    2. Richard M. Everson, Jonathan E. Fieldsend
      Pages 533-556
    3. Stefan Roth, Alexander Gepperth, Christian Igel
      Pages 629-655
  7. Back Matter
    Pages 657-660

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.


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
  • Copyright Information Springer 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-30676-4
  • Online ISBN 978-3-540-33019-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site