SUPClust: Active Learning at the Boundaries

arXiv:2403.03741v1 Announce Type: new
Abstract: Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel active learning method called SUPClust that seeks to identify points at the decision boundary between classes. By targeting these points, SUPClust aims to gather information that is most informative for refining the model's prediction of complex decision regions. We demonstrate experimentally that labeling these points leads …

abstract active learning arxiv cs.ai cs.cv cs.lg data decision identify machine machine learning novel paradigm performance targeting type work