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- W2400604454 abstract "Attribute reduction is an important topic in Decision-Theoretic Rough Set theory. To overcome the limitations of lower-approximation-monotonicity based reduct and cost minimum based reduct, a moderate attribute reduction approach is proposed in this paper, which combines the lower approximation monotonicity criterion and cost minor criterion. Furthermore, the proposed attribute reduct is searched by solving an optimization problem, and a fusion fitness function is proposed in a generic algorithm, such that the reduct is computed in a low time complexity. Experimental analysis is included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm. This study indicates that the optimality is not the best and sub-optimum may be the best choice." @default.
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- W2400604454 date "2015-01-01" @default.
- W2400604454 modified "2023-09-27" @default.
- W2400604454 title "A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set" @default.
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- W2400604454 doi "https://doi.org/10.1007/978-3-319-25783-9_34" @default.
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