Adaptive Active Learning as a Multi-armed Bandit Problem

In this paper, we present a new active learning strategy whose main focus is to have the ability to adapt to the unknown (or changing) learning scenario. We introduce the learners' ensemble based approach and model it as the multi-armed bandit problem. Presented application of simple exploration-exploitation trade-off algorithms from the UCB and EXP3 families show an improvement over using the classical strategies. Evaluation on data from UCI database compare three different selection algorithms. In our tests, presented method shows promising results.