Spherical Fuzzy Sets-Based Cosine Similarity and Information Measures for Pattern Recognition and Medical Diagnosis

Abstract

Spherical fuzzy set (SFS) is a modified version of fuzzy set (FS) to cope with uncertainty and complicated data in real-decision theory. In this article, some similarity measures, called cosine similarity measure (CSM), weighted cosine similarity measure (WCSM), set-theoretic similarity measure (STSM), weighted set-theoretic similarity measure (WSTSM), gray similarity measure (GSM), and weighted gray similarity measure (WGSM) are utilized in the setting of SFSs. Further, the information energy, correlation co-efficient (CC) and weighted correlation co-efficient (WCC) of SFSs are also introduced in this manuscript. The established measures based on SFSs are utilized in the setting of pattern recognition and medical diagnosis to express the validity and reliability of the explored measures with the help of some numerical examples. The projected measures based on SFSs are compared with existing measures, to show that the established measures for SFSs are more generalized than existing measures. The advantages and sensitive analysis of the investigated measures are also discussed in detail.

Keywords

• Correlation co-efficient measures
• information measures
• medical diagnosis
• pattern recognition
• spherical fuzzy sets