Online Choice of Active Learning Algorithms

Yoram Baram, Ran El-Yaniv, and Kobi Luz

This paper is concerned with the question of how to online combine an ensemble of active learners so as to expedite the learning progress during a pool-based active learning session. We develop a powerful active learning master algorithm, based a known competitive algorithm for the multi-armed bandit problem and a novel semi-supervised performance evaluation statistic. Taking an ensemble containing two of the best known active learning algorithms and a new algorithm, the resulting new active learning master algorithm is empirically shown to consistently perform almost as well as and sometimes outperform the best algorithm in the ensemble on a range of classification problems.

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