SUPClust: Active Learning at the Boundaries
Yuta Ono Till Aczel Benjamin Estermann Roger Wattenhofer
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 to strong model performance.
This improvement is observed even in scenarios characterized by strong class
imbalance.