Active Multi-label Learning with Optimal Label Subset Selection

Multi-label classification, where each instance is assigned with multiple labels, has been an attractive research topic in data mining. The annotations of multi-label instances are typically more difficult and time consuming, since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. Study reveals that methods querying instance-label pairs are more effective than those query instances, since for each sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. However, with the high dimensionality of label space, the instance-label pair selective algorithm will be affected since the computational cost of training a multi-label model may be strongly affected by the number of labels. In this paper we propose an approach that combines instance sampling with optimal label subset selection, which can effectively improve the classification model performance and substantially reduce the annotation cost. Experimental results demonstrate the superiority of the proposed approach to state-of-the-art methods on three benchmark datasets.