Label Distribution Learning on Imbalanced Data

Although multi-label learning can handle problems involving label ambiguity, it is not suitable for applications where the overall distribution of the importance of the labels matters. Imbalanced data also poses challenges to classification applications. In this paper, we propose a novel learning paradigm named Label Distribution Learning on Imbalanced Data (LDLID) for such applications, which is based on the improved Kullback-Leibler Divergence and uses the adaptive step size to update the gradient. The experimental results show that LDLID achieves better performance in comparison with four widely-used algorithms on four imbalanced datasets.