Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916923712733184 |
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| author | Wang, Xiaohan Ning, Yang |
| author_facet | Wang, Xiaohan Ning, Yang |
| contents | In this paper, we extend the transfer learning classification framework from regression function-based methods to decision rules. We propose a novel methodology for modeling posterior drift through Bayes decision rules. By exploiting the geometric transformation of the Bayes decision boundary, our method reformulates the problem as a low-dimensional empirical risk minimization problem. Under mild regularity conditions, we establish the consistency of our estimators and derive the risk bounds. Moreover, we illustrate the broad applicability of our method by adapting it to the estimation of optimal individualized treatment rules. Extensive simulation studies and analyses of real-world data further demonstrate both superior performance and robustness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation Wang, Xiaohan Ning, Yang Machine Learning Statistics Theory Methodology In this paper, we extend the transfer learning classification framework from regression function-based methods to decision rules. We propose a novel methodology for modeling posterior drift through Bayes decision rules. By exploiting the geometric transformation of the Bayes decision boundary, our method reformulates the problem as a low-dimensional empirical risk minimization problem. Under mild regularity conditions, we establish the consistency of our estimators and derive the risk bounds. Moreover, we illustrate the broad applicability of our method by adapting it to the estimation of optimal individualized treatment rules. Extensive simulation studies and analyses of real-world data further demonstrate both superior performance and robustness of our approach. |
| title | Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation |
| topic | Machine Learning Statistics Theory Methodology |
| url | https://arxiv.org/abs/2508.20942 |