How to select information that matters: a comparative study on active learning strategies for classification

Abstract

Facing ever increasing volumes of data but limited human annotation capabilities, active learning strategies for selecting the most informative labels gain in importance. However, the choice of an appropriate active learning strategy itself is a complex task that requires to consider different criteria such as the informativeness of the selected labels, the versatility with respect to classification algorithms, or the processing speed. This raises the question, which combinations of active learning strategies and classification algorithms are the most promising to apply. A general answer to this question, without application-specific, label-intensive experiments on each dataset, is highly desirable, as active learning is applied in situations with limited labelled data. Therefore, this paper studies several combinations of different active learning strategies and classification algorithms and evaluates them in a series of comparative experiments.