Parameter Reduction Methods Of Soft Set And Its Extended Model

Soft set is a novel mathematical tool for dealing with uncertainty.Soft sets can also be combined with other mathematical models to expand different models.The soft sets and its extended models have been successfully applied to solve decision problems.However,there exists redundant parameters in the actual decision-making process,and the redundant parameters need to be removed while maintaining the ability to make decisions.This paper proposes two new parameter reduction algorithms for existing algorithms with high complexity and low success rate.The work done in this paper can be summarized into the following three aspects:(1)Parameter reduction method of Chi-Square Distribution in Soft SetThis paper analyzes the proposed normal parameter reduction method and finds that the algorithm has a lower success rate in the actual data set.In order to solve this problem,this paper proposed a parameter reduction method of Chi-square Distribution based on soft set combined with the chi-square formula,which has a higher success rate and practicability compared with the normal parameter reduction algorithm.(2)An Improved parameter reduction method for score decision criteria of fuzzy soft setAiming at the parameter reduction methods of four particular unnecessary sets presented by Kong et al.based on the score decision criteria of fuzzy soft sets,some novel calculation ways of comparison matrix are provided,and then an improved parameter reduction method for score decision criteria in the fuzzy soft set is studied.our algorithm is a fewer than the algorithm of Kong et al.in the algorithm complexity and reduction steps.(3)The application of two reduction methods in the actual environmentThe two reduction methods are applied to the hotel evaluation and the scientific research achievements evaluation respectively.The feasibility and effectiveness of the proposed algorithm are verified by real life cases.