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Multiobjective Optimization in Linguistic Rule Extraction from Numerical Data

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Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

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Abstract

We formulate linguistic rule extraction as a three-objective combinatorial optimization problem. Three objectives are to maximize the performance of an extracted rule set, to minimize the number of extracted rules, and to minimize the total length of extracted rules. The second and third objectives are related to comprehensibility of the extracted rule set. We describe and compare two genetic-algorithm-based approaches for finding nondominated rule sets with respect to the three objectives of our linguistic rule extraction problem. One approach is rule selection where a small number of linguistic rules are selected from prespecified candidate rules. The other is genetics-based machine learning where rule sets are evolved by generating new rules from existing ones using genetic operations.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Osaka Prefecture University, 1-1 Gakuen-cho Sakai, 599-8531, Osaka, Japan

    Hisao Ishibuchi & Tomoharu Nakashima

  2. Department of Industrial and Information Systems Engineering, Ashikaga Institute of Technology, 268 Omae-cho, Ashikaga, 599-8531, 326-8558, Japan

    Tadahiko Murata

Authors
  1. Hisao Ishibuchi
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  2. Tomoharu Nakashima
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  3. Tadahiko Murata
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Ishibuchi, H., Nakashima, T., Murata, T. (2001). Multiobjective Optimization in Linguistic Rule Extraction from Numerical Data. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_41

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  • DOI: https://doi.org/10.1007/3-540-44719-9_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

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