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Genetic-Algorithm-Based Approaches to Classification Problems

  • Chapter
Fuzzy Evolutionary Computation

Abstract

In this chapter, we explain how multi-objective genetic algorithms can be applied to the design of fuzzy rule-based systems for pattern classification problems. For a multi-class pattern classification problem with many continuous attributes (e.g., wine classification with 13 continuous attributes [1]), a fuzzy rule-based classification system is designed by a multi-objective genetic algorithm [2, 3] with two objectives: to minimize the size of the fuzzy rule-based classification system and to maximize its performance [4, 5]. The size of the fuzzy rule-based classification system is expressed by the number of fuzzy if-then rules, and its performance is measured by the number of correctly classified training patterns. In our approach to the design of the fuzzy rule-based classification system, first a large number of fuzzy if-then rules are generated from training patterns as candidate rules for the rule selection. Then a small number of fuzzy if-then rules are selected from the candidate rules by the two-objective genetic algorithm. The two-objective genetic algorithm tries to find all the non-dominated solutions (i.e., non-dominated rule sets) of the rule selection problem with the above-mentioned objectives. One difficulty of the rule selection method for high-dimensional pattern classification problems with many continuous attributes is that the number of candidate fuzzy if-then rules becomes intractably large. For example, the number of candidate rules is 613 ≅ 1.3 × 1010 for the wine classification problem with 13 continuous attributes if we have six fuzzy sets (terms) on each axis of the 13-dimensional pattern space.

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Authors and Affiliations

  1. Department of Industrial Engineering, Osaka Prefecture University, Gakuen-cho 1-1, Sakai, Osaka, 593, Japan

    Hisao Ishibuchi, Tadahiko Murata & Tomoharu Nakashima

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  1. Hisao Ishibuchi
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  2. Tadahiko Murata
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  3. Tomoharu Nakashima
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  1. The University of Manitoba, Winnipeg, Manitoba, Canada

    Witold Pedrycz

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Ishibuchi, H., Murata, T., Nakashima, T. (1997). Genetic-Algorithm-Based Approaches to Classification Problems. In: Pedrycz, W. (eds) Fuzzy Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6135-4_6

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  • DOI: https://doi.org/10.1007/978-1-4615-6135-4_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7811-2

  • Online ISBN: 978-1-4615-6135-4

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