新方法解决决策规则漂移问题，提升个性化治疗方案精准度
Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
- 基于贝叶斯决策边界几何变换建模规则漂移
- 在温和条件下实现估计一致性与风险边界控制
- 适用于个性化治疗规则优化，性能优于传统方法
本文将迁移学习分类框架从基于回归函数的方法拓展至决策规则。提出一种通过贝叶斯决策规则建模后验漂移的新方法。利用贝叶斯决策边界的几何变换，将问题重构成低维经验风险最小化问题。在适度正则条件下，建立了估计量的一致性并推导出风险界。此外，通过适配最优个体化治疗规则估计，展示了该方法的广泛应用性。大量模拟研究和真实数据分析进一步验证了其优越性能与稳健性。
原文摘要 · Abstract (English)
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.
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