  Multilabel classification is an emergent data mining task with a broad range
of real world applications. Learning from imbalanced multilabel data is being
deeply studied latterly, and several resampling methods have been proposed in
the literature. The unequal label distribution in most multilabel datasets,
with disparate imbalance levels, could be a handicap while learning new
classifiers. In addition, this characteristic challenges many of the existent
preprocessing algorithms. Furthermore, the concurrence between imbalanced
labels can make harder the learning from certain labels. These are what we call
\textit{difficult} labels. In this work, the problem of difficult labels is
deeply analyzed, its influence in multilabel classifiers is studied, and a
novel way to solve this problem is proposed. Specific metrics to assess this
trait in multilabel datasets, called \textit{SCUMBLE} (\textit{Score of
ConcUrrence among iMBalanced LabEls}) and \textit{SCUMBLELbl}, are presented
along with REMEDIAL (\textit{REsampling MultilabEl datasets by Decoupling
highly ImbAlanced Labels}), a new algorithm aimed to relax label concurrence.
How to deal with this problem using the R mldr package is also outlined.
