Soft Incomplete Discernibility Matrix for Decision-Making Problems

Many decision-making processes are determined in an incomplete data environment. The analysis of decision-making problems on incomplete soft set data is usually performed by first estimating incomplete values. In this paper, we analyze the decision-making problems of incomplete soft sets with incomplete data without turning them into complete data. We define a soft incomplete discernibility matrix and a soft parameter dominant incomplete discernibility matrix to solve the application of incomplete soft sets in decision-making problems. We focus on the classification ability of the corresponding parameter for a given incomplete soft set. The novel method maintains the original data state and successfully constructs a decision-making approach that can be applied to incomplete soft sets. Finally, the weighted soft incomplete discernibility matrix is defined for application in weighted parameter decision-making issues.