标题： 基于决策规则漂移的分类迁移学习及其在最优个体化治疗规则估计中的应用 显示英文标题

标题： Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation

摘要： 在本文中，我们将基于回归函数的迁移学习分类框架扩展到决策规则。我们提出了一种新的方法来通过贝叶斯决策规则建模后验漂移。通过利用贝叶斯决策边界的几何变换，我们的方法将问题重新表述为低维经验风险最小化问题。在适度的正则性条件下，我们建立了估计量的一致性并推导了风险界限。此外，我们通过将其适应于最优个体化治疗规则的估计，展示了我们方法的广泛适用性。大量的模拟研究和对现实世界数据的分析进一步证明了我们方法的优越性能和鲁棒性。 显示英文摘要

摘要： 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.