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H-max distance measure of intuitionistic fuzzy sets in decision making
Version 2 2024-06-06, 10:46Version 2 2024-06-06, 10:46
Version 1 2019-05-17, 13:43Version 1 2019-05-17, 13:43
journal contribution
posted on 2018-08-01, 00:00 authored by R T Ngan, L H Son, B C Cuong, Mumtaz Ali© 2018 Elsevier B.V. Intuitionistic fuzzy sets (IFSs) are successful to handle the uncertain situations of data. Distance measures of IFSs are important in the evaluation of IFSs relationships. In this paper, we analyzed the disadvantages of existing distance measures of IFSs and proposed a new distance measure called H-max of IFSs. We continued to point out some new results on intuitionistic t-norms and intuitionistic t-conorms and evaluated distance measure between two IFSs which are basically structured from these operations. Further, we combined the classification of t-representable intuitionistic fuzzy t-norms and t-conorms with the proposed distance measure to study some interesting properties. Moreover, we studied De Morgan triplets of IFSs based on the proposed distance measure. Finally, we applied the proposed distance measure to medical diagnosis problem examples and experimental validation on real-world datasets to check the applicability and effectiveness.
History
Journal
Applied soft computing journalVolume
69Pagination
393 - 425Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
1568-4946Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2018, Elsevier B.V.Usage metrics
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Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceIntuitionistic fuzzy setsDistance measureIntuitionistic t-normIntuitionistic t-conorMedical diagnosisSIMILARITY MEASURESENTROPY MEASURESPATTERN-RECOGNITIONINFORMATIONFRAMEWORKNUMBERSInformation SystemsArtificial Intelligence and Image Processing
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