Model selection based on combined penalties for biomarker identification
Model selection based on combined penalties for biomarker identification
复制标题
基于生物标志物识别组合惩罚的模型选择
DOI：
10.1080/10543406.2017.1378662
复制
发表时间：
2018
影响因子：
1.1
通讯作者：
R. Vonk
中科院分区：
文献类型：
--
作者：
E. Vradi;W. Brannath;T. Jaki;R. Vonk
摘要随着靶向药物作用的日益增强，人们越来越关注可操作的生物标志物的开发。然而，用于识别用于在高维数据中分类的生物标志物组的当前惩罚选择方法通常导致需要仔细修剪以用于实际使用的高度复杂的组。在正则化方法的框架中，已经提出了作为L1和L0范数的加权和的惩罚，以考虑所得模型的复杂性。在实践中，这种惩罚的限制是目标函数是非凸的、非光滑的，优化是计算密集型的，并且应用于高维设置是具有挑战性的。本文提出了一种将L0与L1或L2范数相结合的逐步前向变量选择方法。在逐步选择过程中使用的惩罚似然准则导致更简约的模型，仅保留最相关的特征。仿真结果和一个真实的应用表明，我们的方法表现出相当的性能与常见的选择方法的预测性能，同时最大限度地减少所选择的模型中的变量的数量，从而导致一个更简约的模型所期望的。
ABSTRACT The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L1 and L0 norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In this paper, we propose a stepwise forward variable selection method which combines the L0 with L1 or L2 norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Simulation results and a real application show that our approach exhibits a comparable performance with common selection methods with respect to the prediction performance while minimizing the number of variables in the selected model resulting in a more parsimonious model as desired.
DOI：
10.1198/016214506000000735
发表时间：
2006-12-01
影响因子：
3.7
作者：
Zou, Hui
通讯作者：
Zou, Hui