Examining the Effect of Cost Assignment on the Performance of Cost-Based Classification Systems

This paper examines the performance of cost-based fuzzy classification systems for pattern classification problems with misclassification costs. The task here is to minimize the total sum of misclassification costs by fuzzy classification systems. In the cost-based fuzzy classification systems, the consequent class of a fuzzy if-then rule is determined so that the misclassification cost is minimized over the covered training patterns by the antecedent part of the fuzzy if-then rule. On the other hand, the 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 fuzzy if-then rule. The grade of certainty of the fuzzy if-then rules in both fuzzy classification systems is calculated by using the compatibility of training patterns from each class. In computational experiments, we compare the performance of the cost-based fuzzy classification systems with that of the conventional compatibility-based fuzzy classification systems.