supclust
SUPClust: Active Learning at the Boundaries
Ono, Yuta, Aczel, Till, Estermann, Benjamin, Wattenhofer, Roger
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, SUP-Clust 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.
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)