Constructing accurate fuzzy classifiers: A new adaptive method for rule-weight specification

Abstract

Different approaches to design fuzzy rule-based classification systems can be grouped into two main categories: descriptive and accurate. In the descriptive category, the emphasis is on the interpretability of the resulting classifier. The classifier is usually represented by a compact set of short fuzzy rules (i.e., with a few number of antecedent conditions) that make it a suitable tool for knowledge representation. In the accurate category, the generalization ability of the classifier is the main target in the design process and no attempt is made to use understandable fuzzy rules in constructing the rule base. In this paper, we propose a simple and efficient method to construct an accurate fuzzy classification system. We use rule-weight as a simple mechanism to tune the classifier and propose a new method of rule-weight specification for this purpose. Through computer simulations on some data sets from UCI repository, we show that the proposed scheme achieves better prediction accuracy compared with other fuzzy and non-fuzzy rule-based classification systems proposed in the past.