  There is often a mixture of very frequent labels and very infrequent labels
in multi-label datatsets. This variation in label frequency, a type class
imbalance, creates a significant challenge for building efficient multi-label
classification algorithms. In this paper, we tackle this problem by proposing a
minority class oversampling scheme, UCLSO, which integrates Unsupervised
Clustering and Label-Specific data Oversampling. Clustering is performed to
find out the key distinct and locally connected regions of a multi-label
dataset (irrespective of the label information). Next, for each label, we
explore the distributions of minority points in the cluster sets. Only the
minority points within a cluster are used to generate the synthetic minority
points that are used for oversampling. Even though the cluster set is the same
across all labels, the distributions of the synthetic minority points will vary
across the labels. The training dataset is augmented with the set of
label-specific synthetic minority points, and classifiers are trained to
predict the relevance of each label independently. Experiments using 12
multi-label datasets and several multi-label algorithms show that the proposed
method performed very well compared to the other competing algorithms.
