This paper proposes the use of mutual information for feature selection in multi-label classification, a surprisingly almost not studied problem. A pruned problem transformation method is first applied, transforming the multi-label problem into a single-label one. A greedy feature selection procedure based on multidimensional mutual information is then conducted. Results on three databases clearly demonstrate the interest of the approach which allows one to sharply reduce the dimension of the problem and to enhance the performance of classifiers. © 2011 Springer-Verlag.
CITATION STYLE
Doquire, G., & Verleysen, M. (2011). Feature selection for multi-label classification problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6691 LNCS, pp. 9–16). https://doi.org/10.1007/978-3-642-21501-8_2
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