Decision analysis review on the concept of class for bipolar soft set theory

To eliminate uncertainty, all data expressed by decision-makers must be processed correctly. The most useful mathematical models developed for this purpose are hybrid set types. Because they collect all the features of the set types they contain under a single model. In this paper, the bipolar soft rough sets, which are a combination of bipolar soft sets and rough sets, which have been actively preferred in studies aimed at eliminating uncertainty in recent years, have been taken into account. To use the bipolar soft rough sets discussed more actively in the decision-making process, the focus is on handling the data expressed by different decision-makers together. The aim of this paper is to develop the concept of bipolar soft rough classes to aim to highlight this contribution to bipolar soft rough set theory. Thus, the concept of bipolar soft rough classes is introduced and a more efficient decision-making algorithm for uncertainty problems is built. Moreover, many novel concepts such as bipolar soft class, bipolar soft partition and bipolar soft cover were proposed and some properties are examined in detail.

Automated summary

This paper discusses the application of bipolar soft rough sets in eliminating uncertainty and decision-making processes, introduces the concept of bipolar soft rough classes, and proposes a more efficient decision-making algorithm. In addition, several novel concepts are presented and their properties are examined in detail.