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Multi-label feature selection considering label supplementation
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DOI：10.1016/j.patcog.2021.108137.png)
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A B S T R A C T Multi-label feature selection is an efficient technique to alleviate the high dimensionality for multi-label learning. Existing multi-label feature selection methods based on information theory either deal with labels individually or treat all label relationships as redundancy. However, two important and being ig-nored issues are the different effects of label relationships and the dynamic changes of label relationships in measuring different candidate features. To address these issues, we first distinguish three types of label relationships: label independence, label redundancy and label supplementation. Second, we consider the changes of label relationships based on different features. By analyzing the differences and the changes of label relationships, two new methods named LSMFS and MLSMFS are proposed, which extracts all supplementary information and the maximum supplementary information of features for each label from other labels, respectively. Finally, experiments on fifteen benchmark multi-label data sets demonstrate the effectiveness of the proposed methods against nine other methods. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Multi-label learning
Multi-label feature selection
Information theory
Label relationships
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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