IHC-LDL: Imbalanced Hierarchical Classification with Label Distribution Learning

Utilizing multi-label learning(MLL) to solve hierarchical classification problems is a challenging task. Recently a novel paradigm named label distribution learning (LDL) handing label polysemy well can mine the relative importance of different labels in label hierarchy. Although numerous hierarchical MLL and LDL algorithms have been proposed respectively, the combined application of the two lacks in-depth exploration. The approaches constructing the hierarchical label distribution often lack addressing feature space correlation and sample imbalance. To address this problem, we propose a LDL-based imbalanced hierarchical classification approach whose main idea is to construct and learn implicit label distribution with hierarchical information, and decompose it into three sub-tasks: label enhancement(LE), imbalanced resampling and adaptive multi-label learning. In this approach, we design a joint strategy of feature-based label distribution and hierarchical label distribution to enhance label and an adaptive weighting for multi-label learning to tackle the task with unobvious label polysemy. Experimental results on widely used open datasets show the effectiveness of our proposal.