Abstract
This paper proposes a learning algorithm of fuzzy if-then rules for pattern classification problems. In this paper we assume that each training pattern has a weight that can be viewed as the degree of importance in its classification. Fuzzy if-then rules are generated from the weighted training patterns. The antecedent part of fuzzy if-then rules involves fuzzy sets for each attribute value in pattern space. The class and the grade of certainty are used in the consequent part of the fuzzy if-then rules in this paper. The consequent class and the grade of certainty of fuzzy if-then rules are determined by a heuristics using given training patterns. The proposed method adjusts the grade of certainty in an error-correction manner. That is, when a training pattern is misclassified by the generated fuzzy if-then rules, the grade of certainty of the responsible fuzzy if-then rule for the misclassification is decreased while we increase that of the fuzzy rule that should correctly classify the training pattern. Through a series of computational experiments we show the effectiveness of the proposed method for several real-world pattern classification problems.
