Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning

Abstract

In multi-label learning, it is rather expensive to label instances 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. A strong multi-label active learning algorithm usually consists of two crucial elements: a reasonable criterion to evaluate the gain of queried label, and an effective classification model, based on whose prediction the criterion can be accurately computed. In this paper, we first introduce an effective multi-label classification model by combining label ranking with threshold learning, which is incrementally trained to avoid retraining from scratch after every query. Based on this model, we then propose to exploit both uncertainty and diversity in the instance space as well as the label space, and actively query the instance-label pairs which can improve the classification model most. Experimental results demonstrate the superiority of the proposed approach to state-of-the-art methods.