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An axiomatically supported divergence measures for q‐rung orthopair fuzzy sets
Author(s) -
Khan Muhammad Jabir,
Alcantud José Carlos R.,
Kumam Poom,
Kumam Wiyada,
AlKenani Ahmad N.
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22545
Subject(s) - divergence (linguistics) , axiom , entropy (arrow of time) , mathematics , fuzzy set , fuzzy logic , measure (data warehouse) , transformation (genetics) , selection (genetic algorithm) , set (abstract data type) , kullback–leibler divergence , computer science , statistics , artificial intelligence , data mining , physics , geometry , philosophy , linguistics , biochemistry , chemistry , quantum mechanics , gene , programming language
Despite the importance of divergence measures, the literature has not provided a satisfactory formulation for the case of q‐rung orthopair fuzzy set. This paper criticizes the existing attempts in terms of respect of the basic axioms of a divergence measure. Then new improved, axiomatically supported divergence measures for qROFSs are proposed. Additional properties of the new divergence measures are discussed to guarantee their good performance. The transformation relationships with entropy and dissimilarity measures are debated. The multiattribute border approximation area comparison decision method is extended based on the suggested divergence measures, and it is applied to the selection of all‐rounder cricketer for a team.