A novel similarity/dissimilarity measure for intuitionistic fuzzy sets and its application in pattern recognition

Abstract

Among the most interesting measures in intuitionistic fuzzy sets (IFSs) theory, the similarity measure is an essential tool to compare and determine degree of similarity between IFSs. Although there exist many similarity measures for IFSs, most of them cannot satisfy the axioms of similarity measure or provide reasonable results. In this paper, a novel knowledge-based similarity/dissimilarity measure between IFSs is proposed. Firstly, we define a new knowledge measure of information conveyed by the IFS and prove some properties of the proposed knowledge measure. Based on the proposed knowledge measure of IFSs, we construct a novel similarity/dissimilarity measure between IFSs and prove some properties of the proposed similarity measure. Then we use some illustrative examples to show that the proposed measures, though simple in concept and calculus, can overcome the drawbacks of the existing measures. Finally, we apply the proposed similarity/dissimilarity measure between IFSs in the pattern recognition problems to demonstrate that the proposed measure is the most reliable to deal with the pattern recognition problem in comparison with the existing similarity measures.