A novel entropy-based weighted attribute selection in enhanced multicriteria decision-making using fuzzy TOPSIS model fo
- PDF / 1,277,244 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 85 Downloads / 194 Views
ORIGINAL ARTICLE
A novel entropy‑based weighted attribute selection in enhanced multicriteria decision‑making using fuzzy TOPSIS model for hesitant fuzzy rough environment Archana Dikshit‑Ratnaparkhi1 · Dattatraya Bormane2 · Rajesh Ghongade3 Received: 3 March 2020 / Accepted: 6 August 2020 © The Author(s) 2020
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 ( 𝛿 ) 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. Keywords Uncertainity · Hesitant fuzzy sets · Decision-making · Correlation · TOPSIS
Introduction For decades, 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 * Archana Dikshit‑Ratnaparkhi [email protected] Dattatraya Bormane [email protected] Rajesh Ghongade [email protected] 1
Department of Electronics and Telecommunication, Vishwakarma Institute of Information Technology, Affiliated to Savitribai Phule Pune University, Pune, Maharashtra, India
2
AISSMS college of Engineering, Affiliated to Savitribai Phule Pune University, Pune, Maharashtra, India
3
Department of Electronics and Telecommunication, Bharati Vidyapeeth College of Engineering, Pune, Maharashtra, India
of conflicting criteria. The uncertainty and vagueness involved with the human DM process could be effectually modeled by FS theory. MCDM embraces attributes, decision methods, selection criteria, and even subjective estimation of experts [1]. Improvisat
Data Loading...
