Label Expansion for Multi-label Classification

In multi-label classification tasks, instances are simultaneously associated with multiple labels, representing different and, possibly, related concepts from a domain. One characteristic of these tasks is a high class-label imbalance. In order to obtain improved predictive models, several algorithms either have explored the label dependencies or have dealt with the problem of imbalanced labels. This work proposes a label expansion approach which combines both alternatives. For such, some labels are expanded with data from a related class label, making the labels more balanced and representative. Preliminary experiments show the effectiveness of this approach to improve the Binary Relevance strategy. Particularly, it reduced the number of labels that were never predicted in the test instances. Although the results are preliminary, they are potentially attractive, considering the scale and consistency of the improvement obtained, as well as the broad scope of the proposed approach.