A Euclidean Distance-based parameter reduction algorithm for interval-valued fuzzy soft sets

Parameter reduction is a crucial procedure for enhancing the effectiveness of decision-making processes in the theory of interval-valued fuzzy soft set, which is a developing and excellent mathematical tool for processing blurry data. However, there exist severe gaps in the existing literatures. That is, the existing parameter reduction algorithms for interval-valued fuzzy soft sets have high computational complexity and low success rate of finding reduction. By considering these gaps, this study firstly develops the notions of the mean degree of membership and Euclidean distance between parameters. And then a new Euclidean Distance-based parameter reduction algorithm for interval-valued fuzzy soft set is proposed. In addition, we compare our proposed algorithm with the existing parameter reduction algorithms in terms of computational complexity and success rate of finding reduction on 35 randomly generated data sets and a real-life application of Five-Star Hotels evaluation. Through the comparison results, it is clear that our approach has the lower computational complexity and higher success rate of finding reduction. Consequently, our method which is substantiated on the validity and superiority is a good solution for parameter reduction of interval-valued fuzzy soft sets.