Fuzzy Measures and Choquet Integrals Based on Fuzzy Covering Rough Sets

Fuzzy sets and fuzzy rough sets are widely applied in data analysis, data mining, and decision-making. So far, the common method is to use rough approximate operators to induce aggregation functions when fuzzy rough sets are used for multi-criteria decision-making (MCDM). However, they are parametric linear and the corresponding weights are additive measures. In this article, we give a novel method for MCDM based on fuzzy covering rough sets by using the nonadditive measure [i.e., fuzzy measure (FM)] and the nonlinear integral [i.e., Choquet integral (CM)]. First, two nonadditive measures are presented by fuzzy covering lower and upper approximation operators, respectively. Moreover, both of them are FMs which are called beta-neighborhood approximation measures. Second, two types of ChIs with respect to beta-neighborhood approximation measures are constructed. A novel method, which considers the association, is presented to solve the problem of MCDM under the fuzzy covering rough set model. Third, a new approach based on beta-neighborhood approximation measures is proposed for attribute reductions in a fuzzy beta-covering information table. This approach of attribute reductions is used in MCDM. Finally, both new methods above are compared with other methods through some numerical examples and UCI data sets, respectively.

Automated summary

This article proposes a novel method for multi-criteria decision-making based on fuzzy covering rough sets, utilizing nonadditive measures and nonlinear integrals. By introducing fuzzy measures and Choquet integrals, the problem of aggregation function selection and attribute reduction in MCDM is addressed.