Entropy Reweighted Conformal Classification
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917732085137408 |
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| author | Luo, Rui Colombo, Nicolo |
| author_facet | Luo, Rui Colombo, Nicolo |
| contents | Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17377 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entropy Reweighted Conformal Classification Luo, Rui Colombo, Nicolo Machine Learning Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency. |
| title | Entropy Reweighted Conformal Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.17377 |