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

Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets