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Mutual information-based feature selection for multilabel classification
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AI
DOI：10.1016/j.neucom.2013.06.035.png)
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Abstract
En 中文
This paper introduces a new methodology to perform feature selection in multi-label classification problems. Unlike previous works based on the chi(2) statistics, the proposed approach uses the multivariate mutual information criterion combined with a problem transformation and a pruning strategy. This allows us to consider the possible dependencies between the class labels and between the features during the feature selection process. A way to automatically set the pruning parameter is also proposed, based on the permutation test combined with a resampling strategy. Experiments carried out on both artificial and real-world datasets show the interest of our approach over existing methods. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Feature selection
Mutual information
Multi label classification
Problem transformation