  The learning from imbalanced data is a deeply studied problem in standard
classification and, in recent times, also in multilabel classification. A
handful of multilabel resampling methods have been proposed in late years,
aiming to balance the labels distribution. However these methods have to face a
new obstacle, specific for multilabel data, as is the joint appearance of
minority and majority labels in the same data patterns. We proposed recently a
new algorithm designed to decouple imbalanced labels concurring in the same
instance, called REMEDIAL (\textit{REsampling MultilabEl datasets by Decoupling
highly ImbAlanced Labels}). The goal of this work is to propose a procedure to
hybridize this method with some of the best resampling algorithms available in
the literature, including random oversampling, heuristic undersampling and
synthetic sample generation techniques. These hybrid methods are then
empirically analyzed, determining how their behavior is influenced by the label
decoupling process. As a result, a noteworthy set of guidelines on the combined
use of these techniques can be drawn from the conducted experimentation.
