Adapting the CMIM algorithm for multi-label feature selection. A comparison with existing methods
Puerta, Jose M.
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The multi-label paradigm has recently attracted the attention of the machine learning community, multi-label problems being those which do not have only one class but several binomial classes instead. Although intensive research has been carried on lately into the multi-label classification paradigm, this is not the case of feature subset selection methods. In this work we propose an adaptation of the well-known CMIM feature selection algorithm, which is capable of approximating the conditional multivariate mutual information of each candidate attribute with respect to the whole set of labels. This capacity to search any degree of interaction among labels, is the reason why our proposal performs better than other state-of-the-art algorithms when the dataset on which it is run contains correlated labels.