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- W3043140966 abstract "As an intuitive extension of covering-based rough set models, fuzzy β covering provides an effective means to deal with uncertain information. However, classical fuzzy β covering can not fit a given data set well, which limits its popularization and application. On the one hand, this model can not guarantee that the lower approximation is contained in the upper approximation. It does not ideally describe the differences in sample classification. On the other hand, fuzzy β covering is sensitive to noise hidden in data, which leads to unstable classification performance. To address these issues, we set forth a new fitting model for attribute reduction based on fuzzy β covering. To this end, parameterized fuzzy β neighborhoods in terms of a family of fuzzy coverings are introduced to characterize the similarity between samples, using which the granular structures of fuzzy lower and upper approximations of a decision are presented. Fuzzy β covering decision tables are then formalized to deal with knowledge reduction from the viewpoint of maintaining the discriminative ability of a fuzzy covering family. Furthermore, a heuristic attribute reduction algorithm is developed to reduce redundant fuzzy coverings. Extensive numerical experiments are further conducted to examine the effectiveness and feasibility of the proposed model." @default.
- W3043140966 created "2020-07-23" @default.
- W3043140966 creator A5028573018 @default.
- W3043140966 creator A5081963904 @default.
- W3043140966 date "2021-06-01" @default.
- W3043140966 modified "2023-10-14" @default.
- W3043140966 title "A fitting model for attribute reduction with fuzzy β-covering" @default.
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- W3043140966 doi "https://doi.org/10.1016/j.fss.2020.07.010" @default.
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