Mutual information-based feature selection for multilabel classification

Doquire, Gauthier;Verleysen, Michel
(2013) Neurocomputing — Vol. 122, p. 148-155 (2013)

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This paper introduces a new methodology to perform feature selection in multi-label classification problems. Unlike previous works based on the χ2 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.
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Doquire, G., & Verleysen, M. (2013). Mutual information-based feature selection for multilabel classification. Neurocomputing, 122, 148-155. https://doi.org/10.1016/j.neucom.2013.06.035 (Original work published 2013)