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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.08994 |
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| _version_ | 1866909613646938112 |
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| author | Chen, Yan Blanchet, Jose Dembczynski, Krzysztof Nern, Laura Fee Flores, Aaron |
| author_facet | Chen, Yan Blanchet, Jose Dembczynski, Krzysztof Nern, Laura Fee Flores, Aaron |
| contents | Downsampling or under-sampling is a technique that is utilized in the context of large and highly imbalanced classification models. We study optimal downsampling for imbalanced classification using generalized linear models (GLMs). We propose a pseudo maximum likelihood estimator and study its asymptotic normality in the context of increasingly imbalanced populations relative to an increasingly large sample size. We provide theoretical guarantees for the introduced estimator. Additionally, we compute the optimal downsampling rate using a criterion that balances statistical accuracy and computational efficiency. Our numerical experiments, conducted on both synthetic and empirical data, further validate our theoretical results, and demonstrate that the introduced estimator outperforms commonly available alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Downsampling for Imbalanced Classification with Generalized Linear Models Chen, Yan Blanchet, Jose Dembczynski, Krzysztof Nern, Laura Fee Flores, Aaron Machine Learning Downsampling or under-sampling is a technique that is utilized in the context of large and highly imbalanced classification models. We study optimal downsampling for imbalanced classification using generalized linear models (GLMs). We propose a pseudo maximum likelihood estimator and study its asymptotic normality in the context of increasingly imbalanced populations relative to an increasingly large sample size. We provide theoretical guarantees for the introduced estimator. Additionally, we compute the optimal downsampling rate using a criterion that balances statistical accuracy and computational efficiency. Our numerical experiments, conducted on both synthetic and empirical data, further validate our theoretical results, and demonstrate that the introduced estimator outperforms commonly available alternatives. |
| title | Optimal Downsampling for Imbalanced Classification with Generalized Linear Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.08994 |