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- W51264392 abstract "In multi-label learning task, each sample may be assigned with one or more labels. Moreover multi-label classification tasks are often characterized by high-dimensional and inconsistent attributes. Fuzzy rough sets are an effective mathematic tool for dealing with inconsistency and attribute reduction. In this work, we discuss multi-label attribute reduction within the frame of fuzzy rough sets. We analyze the definitions of fuzzy lower approximation in multi-label classification and give several improvements of the traditional algorithms. Furthermore, the attribute dependency function is defined to evaluate condition attributes. A multi-label attribute reduction algorithm is constructed based on the dependency function. Numerical experiments show the effectiveness of the proposed technique." @default.
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- W51264392 date "2014-01-01" @default.
- W51264392 modified "2023-09-26" @default.
- W51264392 title "Multi-label Attribute Evaluation Based on Fuzzy Rough Sets" @default.
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- W51264392 doi "https://doi.org/10.1007/978-3-319-08644-6_10" @default.
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