Novel Pythagorean fuzzy correlation measures via Pythagorean fuzzy deviation, variance and covariance with applications to pattern recognition and career placement

Ejegwa, P; Wen, S; Feng, Y; Zhang, W; Tang, N

Novel Pythagorean fuzzy correlation measures via Pythagorean fuzzy deviation, variance and covariance with applications to pattern recognition and career placement

Ejegwa, P Wen, S

Feng, Y Zhang, W Tang, N

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2022, 30, (6), pp. 1660-1668
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Ejegwa, P
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Feng, Y
dc.contributor.author	Zhang, W
dc.contributor.author	Tang, N
dc.date.accessioned	2023-02-28T04:05:10Z
dc.date.available	2023-02-28T04:05:10Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2022, 30, (6), pp. 1660-1668
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/166547
dc.description.abstract	Pythagorean fuzzy set (PFS) is an importance soft computing tool for curbing embedded vagueness in decision-making. To enhance the applicability of PFSs in modelling practical problems, many computing methods have been studied among which, correlation coefficient is vital. This paper proposes some novel methods of computing correlation between PFSs via the three characteristic parameters of PFSs by incorporating the ideas of Pythagorean fuzzy deviation, variance and covariance. These novel methods evaluate the magnitude of relationship, show the potency of correlation between the PFSs, and also indicate whether the PFSs are related in either positive or negative sense. The proposed techniques are substantiated with some theoretical results, and numerically validated to be superior in terms of accuracy and reliability in contrast to some hitherto similar techniques. Decision-making processes involving pattern recognition and career placement problems are determined with the aid of the proposed techniques.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2021.3063794
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Novel Pythagorean fuzzy correlation measures via Pythagorean fuzzy deviation, variance and covariance with applications to pattern recognition and career placement
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-28T04:05:09Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	6

Abstract:

Pythagorean fuzzy set (PFS) is an importance soft computing tool for curbing embedded vagueness in decision-making. To enhance the applicability of PFSs in modelling practical problems, many computing methods have been studied among which, correlation coefficient is vital. This paper proposes some novel methods of computing correlation between PFSs via the three characteristic parameters of PFSs by incorporating the ideas of Pythagorean fuzzy deviation, variance and covariance. These novel methods evaluate the magnitude of relationship, show the potency of correlation between the PFSs, and also indicate whether the PFSs are related in either positive or negative sense. The proposed techniques are substantiated with some theoretical results, and numerically validated to be superior in terms of accuracy and reliability in contrast to some hitherto similar techniques. Decision-making processes involving pattern recognition and career placement problems are determined with the aid of the proposed techniques.

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http://hdl.handle.net/10453/166547