A Cost-based Fuzzy Rule-based System for Pattern Classification Problems

Abstract

This paper proposes a cost-based fuzzy classification system for pattern classification problems with misclassification costs. The task is to minimize the total misclassification cost incurred by a fuzzy classification system consisting of a number of fuzzy if-then rules where the number of generated fuzzy if-then rules depends on the specification of fuzzy partitions for each axis. In the proposed fuzzy classification system the consequent class of a fuzzy if-then rule is determined so that the misclassification cost is minimal over the covered training patterns by the antecedent part of the rule. On the other hand, conventional fuzzy classification systems are compatibility-based. That is, the consequent class of a fuzzy if-then rule is determined from the compatibility of training patterns covered by the antecedent part of the rule. The grade of certainty of the fuzzy if-then rules in both classification systems is calculated by using the compatibility of training patterns from each class. In a series of computational experiments, we compare the performance of the proposed cost-based fuzzy classification systems with that of the conventional compatibility-based systems. The performance of both classifiers is measured for three real-world pattern classification problems.