稀疏一致性辅助学习的最优治疗决策。

IF 4.3 3区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Journal of Machine Learning Research Pub Date : 2018-04-01

Shuhan Liang, Wenbin Lu, Rui Song, Lan Wang

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引用次数: 0

摘要

为了寻找最优的决策规则，Fan等(2016)提出了一种基于最大秩相关估计的创新的一致性辅助学习算法。它通过两两比较更好地利用了现有信息。然而，目标函数是不连续的，计算上难以优化。在本文中，我们考虑一个凸替代损失函数来解决这个问题。此外，我们的算法保证了决策规则的稀疏性，并且易于解释。导出了超高维下估计系数的l2误差界。各种设置的仿真结果和STAR*D的应用都表明，当协变量数量较大时，所提出的方法仍然可以成功地估计出最优处理方案。

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Sparse concordance-assisted learning for optimal treatment decision.

To find optimal decision rule, Fan et al. (2016) proposed an innovative concordance-assisted learning algorithm which is based on maximum rank correlation estimator. It makes better use of the available information through pairwise comparison. However the objective function is discontinuous and computationally hard to optimize. In this paper, we consider a convex surrogate loss function to solve this problem. In addition, our algorithm ensures sparsity of decision rule and renders easy interpretation. We derive the L ₂ error bound of the estimated coefficients under ultra-high dimension. Simulation results of various settings and application to STAR*D both illustrate that the proposed method can still estimate optimal treatment regime successfully when the number of covariates is large.

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来源期刊

Journal of Machine Learning Research 工程技术-计算机：人工智能

CiteScore

18.80

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.