Acquisition of fuzzy classification knowledge using genetic algorithms
Hisao Ishibuchi, Kenji Nozaki, Naohisa Yamamoto, Hideo Tanaka · 1994
This paper proposes a genetic-algorithm-based approach to the construction of fuzzy classification systems with rectangular fuzzy rules. In the proposed approach, compact fuzzy classification systems are automatically constructed from numerical data by selecting a small number of significant fuzzy rules using genetic algorithms. Since significant fuzzy rules are selected and unnecessary fuzzy rules are removed, the proposed approach can be viewed as a knowledge acquisition tool for classification problems. In this paper, first we describe a generation method of rectangular fuzzy rules from numerical data for classification problems. Next, we formulate a rule selection problem for constructing a compact fuzzy classification system as a combinatorial optimization problem. Then we show how genetic algorithms are applied to the rule selection problem.>