A weighting function for improving fuzzy classification systems performance

Abstract

This paper considers the automatic design of fuzzy rule-based classification systems from labeled data. The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems. We propose a weighting function for compatibility grade of patterns that improves the performance of fuzzy classification system without degrading the interpretability of fuzzy rules. Our approach divides the covering subspace of each fuzzy rule into two subdivisions based on a threshold. Any pattern with compatibility grade above this threshold should be classified truly so the weighting function enhances their association degree. For patterns below threshold, their compatibility grades remain unchanged. The splitting threshold for each rule (i.e. the compatibility grade of a specific pattern) is found using distribution of patterns in the covering subspace of that rule. We also show that how the proposed approach is applicable when fuzzy rules have certainty grades. Experiments on some well-known data sets are used to evaluate the performance of our approach.