A variance maximization criterion for active learning

Yazhou Yang, Marco Loog

Abstract: Highlights•A novel active learning approach is proposed, which measures the value of unlabeled data by its predictive variance.•To measure the informativeness and representativeness of unlabeled instances, two types of variance are introduced.•Excellent performance of the proposed method in comparison with state-of-the-art active learning algorithms is demonstrated.