Cost-Based Fuzzy Rule Generation for Classification Problems

Abstract

In traditional pattern classification where misclassification cost is the same for all patterns, an error rate is often used to evaluate the performance of classification systems. In real world, however, there are important and unimportant patterns. We propose a fuzzy rule-generation method that is based on misclassification cost. We show the effectiveness of the proposed method through computational experiments.