Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection

Excerpt

arXiv:2512.12870v2 Announce Type: replace Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are often noisy due to varying labeler expertise and annotation uncertainty, especially for complex or ambiguous samples. Learning from such imperfectly labeled data can degrade classifier performance. We propose an AL framework that explicitly accounts for label noise by optimally assigning labelers and selecti