Novel Bipolar Soft Rough-Set Approximations and Their Application in Solving Decision-Making Problems

Abstract

The rough set (RS) theory is a successful approach for studying the uncertainty in data. In contrast, the bipolar soft sets (BSS) can deal with the uncertainty, as well as bipolarity of the data in many situations. In 2018, Karaaslan and C ¸agman proposed bipolar soft rough sets (BSRSs), a hybridization of RS and BSS. However, certain shortcomings with BSRS violate Pawlak's RS theory. To overcome these shortcomings, the concept of the modified bipolar soft rough set (MBSRS) has been proposed in this study. Moreover, this idea has been investigated through illustrative examples, where the important properties are inspected deeply. Furthermore, certain significant measures associated with MBSRS are also provided. Finally, an application of the MBSRS to multi-attribute group decision-making (MAGDM) problems is proposed. In addition, among various alternatives, an algorithm for decisionmaking accompanied by a practical example is presented as the optimal alternative . A brief comparative analysis of the proposed approach with some existing techniques is also provided to indicate the validity, flexibility, and superiority of the suggested MAGDM model.