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Interpretability Issues in Fuzzy Genetics-Based Machine Learning for Linguistic Modelling

  • Chapter
Modelling with Words

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2873))

Abstract

This chapter discusses several issues related to the design of linguistic models with high interpretability using fuzzy genetics-based machine learning (GBML) algorithms. We assume that a set of linguistic terms has been given for each variable. Thus our modelling task is to find a small number of fuzzy rules from possible combinations of the given linguistic terms. First we formulate a three-objective optimization problem, which simultaneously minimizes the total squared error, the number of fuzzy rules, and the total rule length. Next we show how fuzzy GBML algorithms can be applied to our problem in the framework of multi-objective optimization as well as single-objective optimization. Then we point out a possibility that misleading fuzzy rules can be generated when general and specific fuzzy rules are simultaneously used in a single linguistic model. Finally we show that non-standard inclusion-based fuzzy reasoning removes such an undesirable possibility.

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Authors and Affiliations

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

    Hisao Ishibuchi & Takashi Yamamoto

Authors
  1. Hisao Ishibuchi
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  2. Takashi Yamamoto
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Editor information

Editors and Affiliations

  1. Artificial Intelligence Group, Engineering Mathematics Department, University of Bristol, BS8 1TR, UK

    Jonathan Lawry

  2. Clairvoyance Corporation, 5301 Fifth Avenue, 15232, Pittsburgh, PA, USA

    Jimi Shanahan

  3. ECECS Department, University of Cincinnati, ML 0030, 45221-0030, Cincinnati, OH, USA

    Anca L. Ralescu

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© 2003 Springer-Verlag Berlin Heidelberg

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Ishibuchi, H., Yamamoto, T. (2003). Interpretability Issues in Fuzzy Genetics-Based Machine Learning for Linguistic Modelling. In: Lawry, J., Shanahan, J., L. Ralescu, A. (eds) Modelling with Words. Lecture Notes in Computer Science(), vol 2873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39906-3_11

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  • DOI: https://doi.org/10.1007/978-3-540-39906-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20487-9

  • Online ISBN: 978-3-540-39906-3

  • eBook Packages: Springer Book Archive

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