Learning Fuzzy If-Then Rules for Pattern Classi cation with Weighted Training Patterns.
Tomoharu Nakashima, Yasuyuki Yokota, Hisao Ishibuchi, Gerald Schaefer · European Society for Fuzzy Logic and Technology Conference · 2005
In this paper we propose a learning method of fuzzy if-then rules for pattern classication problems. We assume that each training pattern has a weight that describes its importance. The antecedent part of fuzzy if-then rules are specied by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty of the fuzzy if-then rules are determined from the compatibility and weights of training patterns. The proposed learning method adjusts the degree of certainty so that the classication cost is minimized. Experimental results on several UCI data sets show the eectiv eness of the proposed method.