期刊
PATTERN RECOGNITION LETTERS
卷 34, 期 3, 页码 349-357出版社
ELSEVIER
DOI: 10.1016/j.patrec.2012.10.005
关键词
Multi-label feature selection; Multivariate feature selection; Multivariate mutual information; Label dependency
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF)
- Ministry of Education, Science and Technology [2012-0001772]
- National Research Foundation of Korea [2010-0012885] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Recently, classification tasks that naturally emerge in multi-label domains, such as text categorization, automatic scene annotation, and gene function prediction, have attracted great interest. As in traditional single-label classification, feature selection plays an important role in multi-label classification. However, recent feature selection methods require preprocessing steps that transform the label set into a single label, resulting in subsequent additional problems. In this paper, we propose a feature selection method for multi-label classification that naturally derives from mutual information between selected features and the label set. The proposed method was applied to several multi-label classification problems and compared with conventional methods. The experimental results demonstrate that the proposed method improves the classification performance to a great extent and has proved to be a useful method in selecting features for multi-label classification problems. (C) 2012 Elsevier B.V. All rights reserved.
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