CCnet: Joint multi-label classification and feature selection using classifier chains and elastic net regularization

Classifier chains are among the most successful methods in multi-label classification due to their simplicity and promising performance. However the standard versions of classifier chains described in the literature do not usually perform feature selection. In this paper we propose an algorithm CCnet which is a combination of classifier chains and elastic-net regularization. An important advantage of the CCnet is that selection of the relevant features in an integral element of the learning process. We show the stability of our algorithm and analyse the generalization error bound. The difference between generalization error and empirical error is bounded by a term which scales as n−1/2, where n is a size of a training data. It follows from experiments that the proposed algorithm outperforms the standard versions of classifier chains as well as other state-of-the-art methods. We also show that the feature selection is stable with respect to the order of fitting the models in the chain.