Covering-based variable precision fuzzy rough sets with PROMETHEE-EDAS methods

This paper proposes a reflexive fuzzy beta-neighborhood operator by modifying Ma's fuzzy beta-neighborhood operators. Then, we use such an operator to build a covering-based variable precision fuzzy rough set (CVPFRS) model that can deal with the issue of misclassifications and perturbations in decision-making problems. By combining the CVPFRS model with two traditional decision-making methods (the PROMETHE method and the DEAS method), we introduce a novel method for addressing multi-attribute decision-making (MADM) problems. An illustrative example is utilized to demonstrate the practicality of the proposed method. The effectiveness of the proposed method is validated by comparing it with existing methods. By virtue of the cross-validation and hypothesis testing, we give an experimental analysis to interpret the validity and stability of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.