SUPClust: Active Learning at the Boundaries
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866913256329707520 |
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| author | Ono, Yuta Aczel, Till Estermann, Benjamin Wattenhofer, Roger |
| author_facet | Ono, Yuta Aczel, Till Estermann, Benjamin Wattenhofer, Roger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SUPClust: Active Learning at the Boundaries Ono, Yuta Aczel, Till Estermann, Benjamin Wattenhofer, Roger Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | SUPClust: Active Learning at the Boundaries |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.03741 |