Introducing Class-Based Classification Priority in Fuzzy Rule-Based Classification Systems

Tomoharu Nakashima, Yasuyuki Yokota, Gerald Schaefer, Hisao Ishibuchi

Abstract: In this paper we propose a fuzzy rule-generation method for pattern classification problems with classification priority. The assumption in this paper is that a classification priority is given a priori in relation to other classes. Our fuzzy rule-based classification system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patterns. The proposed method decides the consequent class of fuzzy if-then rules based on the number of covered training patterns for each class. In computational experiments we first show the effect of introducing classification priority for a synthetic two-dimensional problem. Then we show the effectiveness of the proposed method for several real-world pattern classification problems.