mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms

MultiLabel Learning (MLL) [1] is one of the most common machine learning tasks today. It is based on the idea that each data sample is associated with a certain subset of labels. The full set of labels can be large, in many cases even having more labels than input features. As a result, it is common for some labels to occur in only a few samples, while others occur much more frequently. The label imbalance [2] in MLL is almost always present, and it is a serious obstacle to training good classifiers. Class imbalance is a very well-known problem in traditional learning tasks such as binary and multiclass classification. Hundreds of articles [3, 4, 5], conference papers [6] and books [7] have been devoted to studying it and proposing possible solutions. The most popular are data resampling, cost-sensitive learning and mixtures of these approaches [8, 9]. However, imbalanced learning in the MLL field presents some specific aspects that make it more difficult to deal with this problem.