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• W2569112930 abstract "Multi-label feature selection involves the selection of relevant features from multi-labeled datasets, resulting in a potential improvement of multi-label learning accuracy. In conventional multi-label feature selection methods, the final feature subset is obtained by identifying the features of high relevance with low redundancy. Thus, accurate score evaluation is a key factor for obtaining an effective feature subset. However, conventional methods suffer from inaccurate conditional relevance evaluation when a large number of labels are involved. As a result, irrelevant features can be a member of the final feature subset, leading to low multi-label learning accuracy. In this paper, we propose a new multi-label feature selection method. Using a scalable relevance evaluation process that evaluates conditional relevance more accurately, the proposed method significantly improves multi-label learning accuracy compared with conventional multi-label feature selection methods." @default.
• W2569112930 created "2017-01-13" @default.
• W2569112930 creator A5070821846 @default.
• W2569112930 creator A5080934723 @default.
• W2569112930 date "2017-06-01" @default.
• W2569112930 modified "2023-10-14" @default.
• W2569112930 title "SCLS: Multi-label feature selection based on scalable criterion for large label set" @default.
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• W2569112930 doi "https://doi.org/10.1016/j.patcog.2017.01.014" @default.
• W2569112930 hasPublicationYear "2017" @default.
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