A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems

Ejegwa, PA; Wen, S; Feng, Y; Zhang, W; Liu, J

A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems

Ejegwa, PA Wen, S

Feng, Y Zhang, W Liu, J

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Applied Intelligence, 2023, 53, (1), pp. 226-237
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Ejegwa, PA
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	Liu, J
dc.date.accessioned	2023-03-13T12:57:21Z
dc.date.available	2023-03-13T12:57:21Z
dc.date.issued	2023-01-01
dc.identifier.citation	Applied Intelligence, 2023, 53, (1), pp. 226-237
dc.identifier.issn	0924-669X
dc.identifier.issn	1573-7497
dc.identifier.uri	http://hdl.handle.net/10453/167172
dc.description.abstract	Correlation coefficient (CC) is a reliable information measure for measuring interrelationship between Pythagorean fuzzy sets (PFSs). Some approaches for calculating CC of PFSs have been considered. These hitherto approaches assess only the strength of relationship between PFSs, and are described within the interval [0,1]. This paper proposes a three-way approach for the computation of CC between PFSs by using the concepts of variance and covariance, respectively. This new approach is defined within the interval [− 1,1] akin to classical statistics, shows the strength of relationship between the considered PFSs and indicates whether the PFSs are either positively or negatively correlated. By including the three conventional parameters of PFSs in the proposed technique, the possibility of error due to information leakage is reasonably minimized. The new technique is validated with some theoretical results to show its suitability as reliable information measure. Some numerical examples are considered to show the edges of the new methods over similar methods. From the comparative analysis, the proposed methods of computing CCPFSs give more reliable and reasonable results compare to similar existing methods as presented in Table 13. Certain decision-making problems involving recognition of patterns and diagnostic medicine are resolved with the aid of the new method. The three-way technique of computing correlation coefficient between PFSs can solve decision-making problems that are multi-attributes in nature.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Applied Intelligence
dc.relation.isbasedon	10.1007/s10489-022-03415-5
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems
dc.type	Journal Article
utslib.citation.volume	53
utslib.for	0801 Artificial Intelligence and Image Processing
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	*
dc.date.updated	2023-03-13T12:57:20Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	1

Abstract:

Correlation coefficient (CC) is a reliable information measure for measuring interrelationship between Pythagorean fuzzy sets (PFSs). Some approaches for calculating CC of PFSs have been considered. These hitherto approaches assess only the strength of relationship between PFSs, and are described within the interval [0,1]. This paper proposes a three-way approach for the computation of CC between PFSs by using the concepts of variance and covariance, respectively. This new approach is defined within the interval [− 1,1] akin to classical statistics, shows the strength of relationship between the considered PFSs and indicates whether the PFSs are either positively or negatively correlated. By including the three conventional parameters of PFSs in the proposed technique, the possibility of error due to information leakage is reasonably minimized. The new technique is validated with some theoretical results to show its suitability as reliable information measure. Some numerical examples are considered to show the edges of the new methods over similar methods. From the comparative analysis, the proposed methods of computing CCPFSs give more reliable and reasonable results compare to similar existing methods as presented in Table 13. Certain decision-making problems involving recognition of patterns and diagnostic medicine are resolved with the aid of the new method. The three-way technique of computing correlation coefficient between PFSs can solve decision-making problems that are multi-attributes in nature.

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http://hdl.handle.net/10453/167172