A Novel Approach Towards Parameter Reduction Based on Bipolar Hypersoft Set and Its Application to Decision-Making

Abstract

For a mathematical model to describe vague (uncertain) problems effectively, it must have the
ability to explain the links between the objects and parameters in the problem in the most precise way. There
is no suitable model that can handle such scenarios in the literature. This deficiency serves as motivation for
this study. In this article, the bipolar hypersoft set (abbreviated, BHSS) is considered since the parameters and
their opposite play a symmetrical role. We present a novel theoretical technique for solving decision-making
problems using BHSS and investigate parameter reductions for these sets. Algorithms for parameter reduction
are provided and explained with examples. The findings demonstrate that our suggested parameter reduction
strategies remove unnecessary parameters and still retain the same decision-making options.