  Class-imbalance is an inherent characteristic of multi-label data which
affects the prediction accuracy of most multi-label learning methods. One
efficient strategy to deal with this problem is to employ resampling techniques
before training the classifier. Existing multilabel sampling methods alleviate
the (global) imbalance of multi-label datasets. However, performance
degradation is mainly due to rare subconcepts and overlapping of classes that
could be analysed by looking at the local characteristics of the minority
examples, rather than the imbalance of the whole dataset. We propose a new
method for synthetic oversampling of multi-label data that focuses on local
label distribution to generate more diverse and better labeled instances.
Experimental results on 13 multi-label datasets demonstrate the effectiveness
of the proposed approach in a variety of evaluation measures, particularly in
the case of an ensemble of classifiers trained on repeated samples of the
original data.
