Selecting fuzzy rules with forgetting in fuzzy classification systems
Kenji Nozaki, Hisao Ishibuchi, Hideo Tanaka · 1994
This paper proposes a rule selection method with the destructive learning algorithm to construct a compact fuzzy classification system with high performance. In this paper, first the authors construct a fuzzy classification system by generating fuzzy rules from numerical data, and consider the fuzzy classification system based on fuzzy rules. Then the authors select significant fuzzy rules from the rule set by the proposed method which can remove unnecessary fuzzy rules. The authors demonstrate the effectiveness of the proposed method by applying it to the classification problem of the iris data of Fisher.>