Improving active learning by avoiding ambiguous samples

Abstract

If label information in a classification task is expensive, it can be beneficial to use active learning to get the most informative samples to label by a human. However, there can be samples which are meaningless to the human or recorded wrongly. If these samples are near the classifier’s decision boundary, they are queried repeatedly for labeling. This is inefficient for training because the human can not label these samples correctly and this may lower human acceptance. We introduce an approach to compensate the problem of ambiguous samples by excluding clustered samples from labeling. We compare this approach to other state-of-the-art methods. We further show that we can improve the accuracy in active learning and reduce the number of ambiguous samples queried while training.