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Open Access

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

Department of Mathematics, Lovely Professional University, Punjab 144411, India
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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 analysisfuzzy soft setsDempster-Shafer theoryentropybelief measures and medical diagnosis

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Big Data Mining and Analytics
Volume 4 Issue 2,
June 2021
Pages 94-103
DOI: 10.26599/BDMA.2020.9020020
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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Received: 07 June 2020
Revised: 03 September 2020
Accepted: 04 September 2020
Published: 01 February 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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