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New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis

The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped...

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Autores principales: Gandotra, Neeraj, Kizielewicz, Bartłomiej, Anand, Abhimanyu, Bączkiewicz, Aleksandra, Shekhovtsov, Andrii, Wątróbski, Jarosław, Rezaei, Akbar, Sałabun, Wojciech
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700080/
https://www.ncbi.nlm.nih.gov/pubmed/34945906
http://dx.doi.org/10.3390/e23121600
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author Gandotra, Neeraj
Kizielewicz, Bartłomiej
Anand, Abhimanyu
Bączkiewicz, Aleksandra
Shekhovtsov, Andrii
Wątróbski, Jarosław
Rezaei, Akbar
Sałabun, Wojciech
author_facet Gandotra, Neeraj
Kizielewicz, Bartłomiej
Anand, Abhimanyu
Bączkiewicz, Aleksandra
Shekhovtsov, Andrii
Wątróbski, Jarosław
Rezaei, Akbar
Sałabun, Wojciech
author_sort Gandotra, Neeraj
collection PubMed
description The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped to overcome its imperfections. Its entropy determines the quantity of information in the Pythagorean fuzzy set. Thus, the proposed entropy provides a new flexible tool that is particularly useful in complex multi-criteria problems where uncertain data and inaccurate information are considered. The performance of the introduced method is illustrated in a real-life case study, including a multi-criteria company selection problem. In this example, we provide a numerical illustration to distinguish the entropy measure proposed from some existing entropies used for Pythagorean fuzzy sets and intuitionistic fuzzy sets. Statistical illustrations show that the proposed entropy measures are reliable for demonstrating the degree of fuzziness of both Pythagorean fuzzy set (PFS) and intuitionistic fuzzy sets (IFS). In addition, a multi-criteria decision-making method complex proportional assessment (COPRAS) was also proposed with weights calculated based on the proposed new entropy measure. Finally, to validate the reliability of the results obtained using the proposed entropy, a comparative analysis was performed with a set of carefully selected reference methods containing other generally used entropy measurement methods. The illustrated numerical example proves that the calculation results of the proposed new method are similar to those of several other up-to-date methods.
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spelling pubmed-87000802021-12-24 New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis Gandotra, Neeraj Kizielewicz, Bartłomiej Anand, Abhimanyu Bączkiewicz, Aleksandra Shekhovtsov, Andrii Wątróbski, Jarosław Rezaei, Akbar Sałabun, Wojciech Entropy (Basel) Article The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped to overcome its imperfections. Its entropy determines the quantity of information in the Pythagorean fuzzy set. Thus, the proposed entropy provides a new flexible tool that is particularly useful in complex multi-criteria problems where uncertain data and inaccurate information are considered. The performance of the introduced method is illustrated in a real-life case study, including a multi-criteria company selection problem. In this example, we provide a numerical illustration to distinguish the entropy measure proposed from some existing entropies used for Pythagorean fuzzy sets and intuitionistic fuzzy sets. Statistical illustrations show that the proposed entropy measures are reliable for demonstrating the degree of fuzziness of both Pythagorean fuzzy set (PFS) and intuitionistic fuzzy sets (IFS). In addition, a multi-criteria decision-making method complex proportional assessment (COPRAS) was also proposed with weights calculated based on the proposed new entropy measure. Finally, to validate the reliability of the results obtained using the proposed entropy, a comparative analysis was performed with a set of carefully selected reference methods containing other generally used entropy measurement methods. The illustrated numerical example proves that the calculation results of the proposed new method are similar to those of several other up-to-date methods. MDPI 2021-11-29 /pmc/articles/PMC8700080/ /pubmed/34945906 http://dx.doi.org/10.3390/e23121600 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gandotra, Neeraj
Kizielewicz, Bartłomiej
Anand, Abhimanyu
Bączkiewicz, Aleksandra
Shekhovtsov, Andrii
Wątróbski, Jarosław
Rezaei, Akbar
Sałabun, Wojciech
New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis
title New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis
title_full New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis
title_fullStr New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis
title_full_unstemmed New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis
title_short New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis
title_sort new pythagorean entropy measure with application in multi-criteria decision analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700080/
https://www.ncbi.nlm.nih.gov/pubmed/34945906
http://dx.doi.org/10.3390/e23121600
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