Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 4, Pages 2013-2025Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2014.09.063
Keywords
Multi-label feature selection; Multivariate feature selection; Interaction information; Feature dependency
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Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRP) - Ministry of Education [NRF-2013R1A1A2A10005255]
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Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than single-label feature selection tasks. In this paper, we propose the Mutual Information-based multi-label feature selection method using interaction information. This method is naturally able to measure dependencies among multiple variables. To develop an efficient multi-label feature selection method, we derive theoretical bounds for the interaction information. Empirical studies indicate that our proposed multi-label feature selection method discovers effective feature subsets for multi-label classification problems. (C) 2014 Elsevier Ltd. All rights reserved.
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