FGRBC: A Novel Fuzzy Granular Rule-Based Classifier Using the Justifiable Granularity Principle and a Fusion Strategy

Abstract

As a powerful tool for the representation of classifying knowledge, fuzzy classification rules can not only effectively deal with imprecise and uncertain data, but also possess readability and interpretability. Fuzzy granular rules, also to be a kind of fuzzy classification rules, can be induced by fuzzy information granules. It has been acknowledged that one of the important criteria for evaluating the quality of information granules comes from the principle of justifiable granularity. Unfortunately, the existing methods for extracting fuzzy granular rules fail to take into account the principle of justifiable granularity. In view of the advantages of the justifiable granularity principle in classifying knowledge, we propose in this article a new method of extracting fuzzy granular rules using the justifiable granularity principle and a fusion strategy and establish a fuzzy granular rule-based classifier (FGRBC). Specifically, the justifiability of fuzzy granules is first presented according to both coverage and specificity of fuzzy granules, on which a rule extraction method is formulated to obtain a set of fuzzy granular rules. Furthermore, a fusion strategy is put forward to generate a set of fused rules. Then, the two sets of rules are combined and attribute reduction is performed on the combined rule set. Finally, the reduced combined rule set is employed to construct FGRBC. Moreover, performance of FGRBC is evaluated by numerical experiments and the results show that FGRBC is of satisfactory classification ability.