Towards Class-Imbalance Aware Multi-Label Learning

In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i.e. the widely-existing class-imbalance among labels, has not been well investigated. Generally, the number of positive training instances w.r.t. each class label is far less than its negative counterparts, which may lead to performance degradation for most multi-label learning techniques. In this paper, a new multi-label learning approach named Cross-Coupling Aggregation (COCOA) is proposed, which aims at lever-aging the exploitation of label correlations as well as the exploration of class-imbalance. Briefly, to induce the predictive model on each class label, one binary-class imbalance learner corresponding to the current label and several multi-class imbalance learners coupling with other labels are aggregated for prediction. Extensive experiments clearly validate the effectiveness of the proposed approach, especially in terms of imbalance-specific evaluation metrics such as F-measure and area under the ROC curve.