Parameter Reduction of Fuzzy Soft Sets: An Adjustable Approach Based on the Three-Way Decision

Parameter reduction is one of the major steps in decision-making problems. It refers to determine a minimal subset of a parameter set which preserves the final decision based on the whole set of parameters. The applicability of soft set (SS) theory to bring out a pattern for parameter reduction is discussed by some researchers. Several algorithms have been proposed for computing a reduction of a soft set; however, they have some drawbacks and limitations. These methods, that are based on the binary-decision rules, are usually inspired from the rough set (RS) technique for deleting dispensable parameters, while the difference between SS theory and RS theory has not received any significant attention. This paper studies a new approach for parameter reduction based on the three-way decision methodology under fuzzy soft models. We first review some existing approaches for reduction of a soft set. Then, we design our algorithm for parameter reduction of fuzzy soft sets according to results of Khameneh et al. (Int J Fuzzy Syst, 2016. https://doi.org/10.1007/s40815-016-0280-z) paper. The comparison results on a common dataset show the efficiency of our proposed algorithm.