We consider the problem of online multiclass prediction in the bandit setting. Compared with the full-information setting, in which the learner can receive the true label as feedback after making each prediction, the bandit setting assumes that the learner can only know the correctness of the predicted label. Because the bandit setting is more restricted, it is difficult to design good bandit learners and currently there are not many bandit learners. In this paper, we propose an approach that systematically converts existing online binary classifiers to promising bandit learners with strong theoretical guarantee. The approach matches the idea of boosting, which has been shown to be powerful for batch learning as well as online learning. In particular, we establish the weak-learning condition on the online binary classifier, and show that the condition allows automatically constructing a bandit learner with arbitrary strength by combining several of those classifiers. Experimental results on several real-world data sets demonstrate the effectiveness of the proposed approach.
['Shang-Tse Chen', 'Hsuan-Tien Lin', 'Chi-Jen Lu']
Boosting with Online Binary Learners for the Multiclass Bandit Problem