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• W2772296919 abstract "Abstract Feature selection for multi-label learning has received intensive interest in recent years. However, traditional multi-label feature selection are incapable of considering intrinsic group structures of features and handling streaming features simultaneously. To solve this problem, we develop an algorithm called Online Multi-label Group Feature Selection (OMGFS). Our proposed method consists of two-phase: online group selection and online inter-group selection. In the group selection, we design a criterion to select feature groups which is important to label set. In the inter-group selection, we consider feature interaction and feature redundancy to select an optimal feature subset. This two-phase procedure continues until there are no more features arriving. An empirical study using a series of benchmark data sets demonstrates that the proposed method outperforms other state-of-the-art multi-label feature selection methods." @default.
• W2772296919 created "2017-12-22" @default.
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• W2772296919 date "2018-03-01" @default.
• W2772296919 modified "2023-10-18" @default.
• W2772296919 title "Online Multi-label Group Feature Selection" @default.
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• W2772296919 doi "https://doi.org/10.1016/j.knosys.2017.12.008" @default.
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