Abstract | ||
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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 multicriteria 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 (ChI)]. First, two nonadditive measures are presented by fuzzy covering lower and upper approximation operators, respectively. Moreover, both of them are FMs which are called
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-neighborhood approximation measures. Second, two types of ChIs with respect to
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-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
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-neighborhood approximation measures is proposed for attribute reductions in a fuzzy
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-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 datasets, respectively. |
Year | DOI | Venue |
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2022 | 10.1109/TFUZZ.2021.3081916 | IEEE Transactions on Fuzzy Systems |
Keywords | DocType | Volume |
Choquet integral,covering-based rough set,fuzzy set,neighborhood approximation measure,reduction | Journal | 30 |
Issue | ISSN | Citations |
7 | 1063-6706 | 0 |
PageRank | References | Authors |
0.34 | 39 | 4 |
Name | Order | Citations | PageRank |
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Xiaohong Zhang | 1 | 286 | 31.55 |
Jingqian Wang | 2 | 6 | 2.08 |
Jianming Zhan | 3 | 0 | 0.34 |
Jianhua Dai | 4 | 896 | 51.62 |