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Big Data Mining and Analytics 2021, 4(2): 94-103 https://doi.org/10.26599/BDMA.2020.9020020
Open Access | Issue | Published: 01 February 2021

An Advanced Uncertainty Measure Using Fuzzy Soft Sets: Application to Decision-Making Problems

Show Author's Information Nitin Bhardwaj( ),  Pallvi Sharma
Department of Mathematics, Lovely Professional University, Punjab 144411, India
Keywords:
grey relational analysis, fuzzy soft sets, Dempster-Shafer theory, entropy, belief measures and medical diagnosis
Cite this article:
Bhardwaj N, Sharma P. An Advanced Uncertainty Measure Using Fuzzy Soft Sets: Application to Decision-Making Problems. Big Data Mining and Analytics, 2021, 4(2): 94-103. https://doi.org/10.26599/BDMA.2020.9020020
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Abstract Full text About this article

In this paper, uncertainty has been measured in the form of fuzziness which arises due to imprecise boundaries of fuzzy sets. Uncertainty caused due to human’s cognition can be decreased by the use of fuzzy soft sets. There are different approaches to deal with the measurement of uncertainty. The method we proposed uses fuzzified evidence theory to calculate total degree of fuzziness of the parameters. It consists of mainly four parts. The first part is to measure uncertainties of parameters using fuzzy soft sets and then to modulate the uncertainties calculated. Afterward, the appropriate basic probability assignments with respect to each parameter are produced. In the last, we use Dempster’s rule of combination to fuse independent parameters into integrated one. To validate the proposed method, we perform an experiment and compare our outputs with grey relational analysis method. Also, a medical diagnosis application in reference to COVID-19 has been given to show the effectiveness of advanced method by comparing with other method.


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About this article

An Advanced Uncertainty Measure Using Fuzzy Soft Sets: Application to Decision-Making Problems

Show Author's information Nitin Bhardwaj( ), Pallvi Sharma
Department of Mathematics, Lovely Professional University, Punjab 144411, India

Abstract

In this paper, uncertainty has been measured in the form of fuzziness which arises due to imprecise boundaries of fuzzy sets. Uncertainty caused due to human’s cognition can be decreased by the use of fuzzy soft sets. There are different approaches to deal with the measurement of uncertainty. The method we proposed uses fuzzified evidence theory to calculate total degree of fuzziness of the parameters. It consists of mainly four parts. The first part is to measure uncertainties of parameters using fuzzy soft sets and then to modulate the uncertainties calculated. Afterward, the appropriate basic probability assignments with respect to each parameter are produced. In the last, we use Dempster’s rule of combination to fuse independent parameters into integrated one. To validate the proposed method, we perform an experiment and compare our outputs with grey relational analysis method. Also, a medical diagnosis application in reference to COVID-19 has been given to show the effectiveness of advanced method by comparing with other method.

Keywords: grey relational analysis, fuzzy soft sets, Dempster-Shafer theory, entropy, belief measures and medical diagnosis

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Received: 07 June 2020
Revised: 03 September 2020
Accepted: 04 September 2020
Published: 01 February 2021
Issue date: June 2021

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