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The learning from imbalanced data is a deeply studied problem in standardclassification and, in recent times, also in multilabel classification. Ahandful of multilabel resampling methods have been proposed in late years,aiming to balance the labels distribution. However these methods have to face anew obstacle, specific for multilabel data, as is the joint appearance ofminority and majority labels in the same data patterns. We proposed recently anew algorithm designed to decouple imbalanced labels concurring in the sameinstance, called REMEDIAL (\textit{REsampling MultilabEl datasets by Decouplinghighly ImbAlanced Labels}). The goal of this work is to propose a procedure tohybridize this method with some of the best resampling algorithms available inthe literature, including random oversampling, heuristic undersampling andsynthetic sample generation techniques. These hybrid methods are thenempirically analyzed, determining how their behavior is influenced by the labeldecoupling process. As a result, a noteworthy set of guidelines on the combineduse of these techniques can be drawn from the conducted experimentation.

Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization