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Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems
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DOI：10.1016/S0165-0114(96)00098-X.png)
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Abstract
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This paper proposes various methods for constructing a compact fuzzy classification system consisting of a small number of linguistic classification rules. First we formulate a rule selection problem of linguistic classification rules with two objectives: to maximize the number of correctly classified training patterns and to minimize the number of selected rules. Next we propose three methods for finding a set of non-dominated solutions of the rule selection problem. These three methods are based on a single-objective genetic algorithm. We also propose a method based on a multi-objective genetic algorithm for finding a set of non-dominated solutions. We examine the performance of the proposed methods by applying them to the well-known iris data. Finally we propose a hybrid algorithm by combining a learning method of linguistic classification rules with the multi-objective genetic algorithm. High performance of the hybrid algorithm is demonstrated by computer simulations on the iris data. (C) 1997 Elsevier Science B.V.
Keywords:
linguistic modeling
pattern recognition
data analysis
genetic algorithms
rule selection
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