Determining Consequent Class of Fuzzy If-Then Rules from Weighted Training Patterns

Abstract

In many pattern classification literatures an error rate is often used to evaluate the performance of classification systems because a misclassification cost is the same for all patterns. However, it is necessary to consider the existence of important and unimportant patterns. That is, important patterns have a high misclassification cost while misclassification cost is low for unimportant patterns. We propose a fuzzy rule-generation method based on the misclassification cost. We show the effectiveness of the proposed method through computational experiments.