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. 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.

AnchorAL 是一种针对不平衡分类任务的主动学习方法，通过选择类特定的样本作为锚点，并从未标记数据池中获取最相似的样本来构建子池，从而解决了大型数据池上传统基于池的主动学习的计算复杂度高、准确率低的问题，并促进了对少数类实例的发现与类平衡。