Cost-effective Batch-mode Multi-label Active Learning

A meta-framework for multi-label active learning based on deep reinforcement learning

Multi-label Active Learning (MLAL) is an effective method to improve the performance of the classifier on multi-label problems with less annotation effort by allowing the learning system to actively select high-quality examples (example-label pairs) for labeling. Existing MLAL algorithms mainly focus on designing reasonable algorithms to evaluate the potential values (as previously mentioned quality) of the unlabeled data. These manually designed methods may show totally different results on various types of datasets due to the defect of the methods or the particularity of the datasets. In this paper, instead of manually designing an evaluation method, we propose a deep reinforcement learning (DRL) model to explore a general evaluation method on several seen datasets and eventually apply it to unseen datasets based on a meta framework. In addition, a self-attention mechanism along with a reward function is integrated into the DRL structure to address the label correlation and data imbalanced problems in MLAL. Comprehensive experiments show that our proposed DRL-based MLAL method is able to produce comparable results as compared with other methods reported in the literature.