期刊
PATTERN RECOGNITION
卷 48, 期 9, 页码 2761-2771出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.04.009
关键词
Multi-label feature selection; Mutual information; Interaction information; Entropy
资金
- Basic Science Research Program through National Research Foundation of Korea (NRF) - Ministry of Education [2013R1A1A2A10005255]
- National Research Foundation of Korea [2013R1A1A2A10005255, 22A20130012014] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Multi-label feature selection involves selecting important features from multi-label data sets. This can be achieved by ranking features based on their importance and then selecting the top-ranked features. Many multi-label feature selection methods for finding a feature subset that can improve multi-label learning accuracy have been proposed. In contrast, computationally efficient multi-label feature selection methods have not been studied extensively. In this study, we propose a fast multi-label feature selection method based on information-theoretic feature ranking. Experimental results demonstrate that the proposed method generates a feature subset significantly faster than several other multilabel feature selection methods for large multi-label data sets. (C) 2015 Elsevier Ltd. All rights reserved.
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