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Some modified Pythagorean fuzzy correlation measures with application in determining some selected decision-making problems

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Abstract

Pythagorean fuzzy set (PFS) is an advanced version of generalized fuzzy sets. It has a better applicative expression in decision-making because of its capability in curbing fuzziness embedded in decision science. Correlation coefficient is a reliable measuring operator for the applicability of generalized fuzzy sets in decision-making. Some approaches of estimating correlation of PFSs have been explored, albeit with certain setbacks. This paper introduces some methods of calculating the correlation coefficient of PFSs which resolve the setbacks in the existing methods. Some numerical examples are supplied to confirm the superiority of the novel methods over the existing correlation coefficient measures. In addition, certain decision-making problems such as marital choice-making, classification of building materials, and electioneering process represented in Pythagorean fuzzy values are resolved using the proposed correlation measure. Specifically, the objectives of this work are to (1) introduce some new triparametric methods of computing correlation coefficient of PFSs, (2) characterize their theoretic properties, (3) ascertain their advantages over the existing methods, and (4) explore the application of the proposed methods in certain decision-making problems. From the study, it is observed that the new Pythagorean fuzzy correlation coefficients give reliable outputs compared to the existing ones and, hence, can suitably handle multiple criteria decision-making effectively.

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  1. Department of Mathematics/Statistics/Computer Science, University of Agriculture, P.M.B. 2373, Makurdi, Nigeria

    Paul Augustine Ejegwa, Victoria Adah & Idoko Charles Onyeke

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  1. Paul Augustine Ejegwa
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  2. Victoria Adah
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  3. Idoko Charles Onyeke
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Correspondence to Paul Augustine Ejegwa.

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Ejegwa, P.A., Adah, V. & Onyeke, I.C. Some modified Pythagorean fuzzy correlation measures with application in determining some selected decision-making problems. Granul. Comput. 7, 381–391 (2022). https://doi.org/10.1007/s41066-021-00272-4

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