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Entropy 2016, 18(11), 405; doi:10.3390/e18110405

Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Korea
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Academic Editor: Kevin H. Knuth
Received: 15 July 2016 / Revised: 4 October 2016 / Accepted: 10 November 2016 / Published: 15 November 2016
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Abstract

Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets. View Full-Text
Keywords: multi-label feature selection; label selection; mutual information; entropy multi-label feature selection; label selection; mutual information; entropy
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Lee, J.; Kim, D.-W. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection. Entropy 2016, 18, 405.

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