Efficient Multi-label Classification with Many Labels

Multi-label classification deals with the problem where each instance can be associated with a set of class labels. However, in many real-world applications, the number of class labels can be in the hundreds or even thousands, and existing multi-label classification methods often become computationally inefficient. In recent years, a number of remedies have been proposed. However, they are either based on simple dimension reduction techniques or involve expensive optimization problems. In this paper, we address this problem by selecting a small subset of class labels that can approximately span the original label space. This is performed by randomized sampling where the sampling probability of each class label reflects its importance among all the labels. Theoretical analysis shows that this randomized sampling approach is highly efficient. Experiments on a number of real-world multi-label datasets with many labels demonstrate the appealing performance and efficiency of the proposed algorithm.