A Novel Multi-Criteria Decision-Making Method Based on Rough Sets and Fuzzy Measures

Rough set theory provides a useful tool for data analysis, data mining and decision making. For multi-criteria decision making (MCDM), rough sets are used to obtain decision rules by reducing attributes and objects. However, different reduction methods correspond to different rules, which will influence the decision result. To solve this problem, we propose a novel method for MCDM based on rough sets and a fuzzy measure in this paper. Firstly, a type of non-additive measure of attributes is presented by the importance degree in rough sets, which is a fuzzy measure and called an attribute measure. Secondly, for a decision information system, the notion of the matching degree between two objects is presented under an attribute. Thirdly, based on the notions of the attribute measure and matching degree, a Choquet integral is constructed. Moreover, a novel MCDM method is presented by the Choquet integral. Finally, the presented method is compared with other methods through a numerical example, which is used to illustrate the feasibility and effectiveness of our method.

Automated summary

Rough set theory is a useful tool for data analysis, data mining, and decision-making. This paper proposes a novel method for multi-criteria decision making based on rough sets and a fuzzy measure. The method utilizes attribute measures and matching degrees to construct a Choquet integral, providing a solution to the problem of different reduction methods affecting decision results. A numerical example is used to demonstrate the feasibility and effectiveness of the proposed method.