ALBIF: Active Learning with BandIt Feedbacks

Mudit Agarwal, Naresh Manwani

Abstract: Online active learning algorithms reduce human labeling costs by querying only a subset of informative incoming instances from the data stream to update the classification model. Active learning for online multiclass classification under complete information has been well addressed; however, it remains unaddressed for the bandit setting. In this paper, we investigate online active learning techniques under the bandit feedback setting. We proposed an efficient algorithm for learning a multiclass classifier with bandit feedbacks under the active learning setting. The proposed algorithms enjoy a regret bound of the order $$\mathcal {O}(\log {T})$$ in the active learning setting as well as in the standard (non-active) bandit feedbacks. We show the effectiveness of the proposed approach using extensive experiments on several benchmark datasets.