This paper presents a practical method for pool-based active learning that is robust to annotation noise. Our work is inspired by recent approaches to active learning in two different noise-free settings: importance-weighted methods for streams and unbiased pool-based techniques. In our proposed method, we employ an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. We demonstrate, using several standard datasets, that the proposed approach, which employs label prediction in combination with importance-weighting, significantly improves active learning in the presence of annotation noise. Moreover, the ease with which the proposed method can be implemented should make it widely applicable to a broad range of real-world applications.
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