Divergence measure of Pythagorean fuzzy sets and its application in medical diagnosis

• Published in: Applied soft computing Vol. 79; pp. 254 - 267
• Main Authors: Xiao, Fuyuan, Ding, Weiping
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2019
• Subjects: Distance measure; Jensen–Shannon divergence; Medical diagnosis; Pythagorean fuzzy sets; Jensen–Shannon divergence; Medical diagnosis; Pythagorean fuzzy sets; Distance measure
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.03.043

The Pythagorean fuzzy set (PFS) which is an extension of intuitionistic fuzzy set, is more capable of expressing and handling the uncertainty under uncertain environments, so that it was broadly applied in a variety of fields. Whereas, how to measure PFSs’ distance appropriately is still an open issue. It is well known that the square root of Jensen–Shannon divergence is a true metric in the probability distribution space which is a useful measure of distance. On account of this point, a novel divergence measure between PFSs is proposed by taking advantage of the Jensen–Shannon divergence in this paper, called as PFSJS distance. This is the first work to consider the divergence of PFSs for measuring the discrepancy of data from the perspective of the relative entropy. The new PFSJS distance measure has some desirable merits, in which it meets the distance measurement axiom and can better indicate the discrimination degree of PFSs. Then, numerical examples demonstrate that the PFSJS distance can avoid generating counter-intuitive results which is more feasible, reasonable and superior than existing distance measures. Additionally, a new algorithm based on the PFSJS distance measure is designed to solve the problems of medical diagnosis. By comparing the different methods in the medical diagnosis application, it is found that the new algorithm is as efficient as the other methods. These results prove that the proposed method is practical in dealing with the medical diagnosis problems.