Learning of fuzzy classification rules by a genetic algorithm
Hisao Ishibuchi, Tadahiko Murata · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 1997
For function approximation problems using fuzzy rules, Nomura and colleagues proposed a method of fuzzy partitioning of an input space by a genetic algorithm. In this paper, we apply their method to pattern classification problems. We extend the coding method in their work, which could handle only triangular fuzzy sets, to the case in which intervals and trapezoidal fuzzy sets can be used as antecedent fuzzy sets. By this extension, a “don't care attribute” can be handled, and the number of fuzzy rules required for the pattern classification can be reduced. To efficiently decrease the number of fuzzy rules, different mutation probabilities are assigned to mutation operations for increasing and for decreasing the number of membership functions in our genetic algorithm. An additional mutation operation is also introduced for fine-tuning the shape of each membership function. Moreover we propose a hybrid algorithm that simultaneously adjusts antecedent fuzzy sets and the grade of certainty of each fuzzy classification rule. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(3): 37–46, 1997