A near-optimal non-myopic active learning method

Non-myopic active learning allows the learner to select multiple unlabeled samples at a time. It avoids tedious retraining with each selected sample, and is effective to utilize multiple labelers. But current non-myopic active learning methods are typically greedy by selecting top N unlabeled samples with maximum score. While efficient, such a greedy active learning approach cannot guarantee the learner´s performance. In this paper, we introduce a near-optimal non-myopic active learning algorithm that is efficient and simultaneously has a performance guarantee. Our experimental results on UCI data sets and a real-world application show that the proposed algorithm outperforms the myopic active learning method and the existing non-myopic active learning methods in both efficiency and accuracy.