Meta-Learning for Batch Mode Active Learning

Active learning involves selecting unlabeled data items to label in order to best improve an existing classifier. In most applications, batch mode active learning, where a set of items is picked all at once to be labeled and then used to re-train the classifier, is most feasible because it does not require the model to be re-trained after each individual selection and makes most efficient use of human labor for annotation. In this work, we explore using meta-learning to learn an active learning algorithm that selects the best set of unlabeled items to label given a classifier trained on a small training set. Our experiments show that our learned active learning algorithm is able to construct labeled sets that improve a classifier better than commonly used heuristics.