A novel entropy-based weighted attribute selection in enhanced multicriteria decision-making using fuzzy TOPSIS model for hesitant fuzzy rough environment

Archana Dikshit-Ratnaparkhi,Rajesh Ghongade,Dattatraya Bormane

doi:10.1007/s40747-020-00187-8

Abstract

The existing approaches of multicriteria decision-making (MCDM) process might yield unreliable and questionable results. The notable challenges of MCDM approaches are rank reversal paradox and uncertainty. The prime inspiration for researchers is the MCDM for hesitant fuzzy sets (HFSs). In some scenarios, the decision-makers could not choose one from numerous values while expressing their preferences. HFS which is the extension of fuzzy sets (FS) is found to be helpful in solving such decision-making (DM) problems. The DM process is revolutionized with the commencement of powerful and efficient tools of data representation for expressing vagueness and uncertainty in data sets as FSs (both generalized and hesitant ones). This paper copes with one such novel approach that involves entropy-based attribute weighting, followed by an evaluation of approximate sets in the fuzzy rough framework. Correlation of the input alternatives in respect of evaluation criteria and the output class is evaluated. With the fuzzy technique for ordered preference by similarity to ideal solutions (FTOPSIS), the generated correlation matrix is utilized for calculating the degree of closeness ( delta ) of the output classes to the input alternatives. This paper made a novel contribution of performance indicator centered on FTOPSIS for the hesitant fuzzy rough domain. The proposed method’s efficiency is established through comprehensive and systematic experimentation on datasets utilized by researchers globally. The proposed algorithms prove its ability to handle datasets that involve human-like hesitant thinking in the MCDM system by contrasting with the existing ones.

Highlights

multicriteria decision-making (MCDM) has remained as an inexorable topic of research
This paper has brought about a pioneering work in the fuzzy rough sets (FRS) field as it bridges the gap from Rough Sets (RS) to hesitant fuzzy sets (HFSs) for attribute reduction
The novelty exists in rendering weighted entropy centered optimum attribute selection method for assessing correlation of the input alternatives with the output class in HFR domain

Summary

Introduction

MCDM has remained as an inexorable topic of research. Optimum selection of alternatives considerably affects the DM of picking a suitable one from a provided set. In some real-life scenarios, on account of the higher uncertainty of the situation and the restricted cognition of human thinking, it is hard for decision-makers to make a choice in selecting merely one alternative as of a candidate alternative set or evaluation arguments set to show their preference They might highly hesitate amongst several alternatives or evaluation arguments. This paper has brought about a pioneering work in the FRSs field as it bridges the gap from RSs to HFSs for attribute reduction It can elevate the DM efficiency and lessen the decision pressure, because, here, the decision-makers are permitted to express their preference in form of entropy centered weighted attribute selection.

(21) Methodology

A2 A3 A4

Conclusion