AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets

arXiv:2404.05623v1 Announce Type: new
Abstract: Active learning for imbalanced classification tasks is challenging as the minority classes naturally occur rarely. Gathering a large pool of unlabelled data is thus essential to capture minority instances. Standard pool-based active learning is computationally expensive on large pools and often reaches low accuracy by overfitting the initial decision boundary, thus failing to explore the input space and find minority instances. To address these issues we propose AnchorAL. At each iteration, AnchorAL chooses class-specific instances …

abstract accuracy active learning arxiv classification cs.cl cs.lg data datasets instances low pool standard tasks type