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Intuitionistic fuzzy statistical correlation algorithm with applications to multicriteria‐based decision‐making processes
Author(s) -
Ejegwa Paul Augustine,
Onyeke Idoko Charles
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.22347
Subject(s) - computer science , correlation , similarity (geometry) , fuzzy set , set (abstract data type) , algorithm , fuzzy logic , measure (data warehouse) , mathematics , data mining , artificial intelligence , geometry , image (mathematics) , programming language
Intuitionistic fuzzy set is a significance soft computing tool for curbing fuzziness embedded in decision‐making processes. To enhance the applicability of intuitionistic fuzzy sets in modelling practical real‐life problems, various computing methods have been proposed like distance measures, similarity measures and correlation measures. This paper proposes an intuitionistic fuzzy statistical correlation algorithm with applications to pattern recognition and diagnostic processes. This novel method assesses the magnitude of relationship and indicates whether the intuitionistic fuzzy sets under consideration are correlated in either positive or negative sense. We substantiate the proposed technique with some theoretical results and numerically validate it to be superior in terms of accuracy and reliability in contrast to some hitherto techniques. Finally, we determine decision‐making processes involving pattern recognition and diagnostic processes by using JAVA programming language to code the intuitionistic fuzzy statistical correlation measure.