  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
from the labelled set, or anchors, and retrieves the most similar unlabelled
instances from the pool. This resulting subpool is then used for active
learning. Using a small, fixed-sized subpool AnchorAL allows scaling any active
learning strategy to large pools. By dynamically selecting different anchors at
each iteration it promotes class balance and prevents overfitting the initial
decision boundary, thus promoting the discovery of new clusters of minority
instances. In experiments across different classification tasks, active
learning strategies, and model architectures AnchorAL is (i) faster, often
reducing runtime from hours to minutes, (ii) trains more performant models,
(iii) and returns more balanced datasets than competing methods.
