论文标题
使用深度学习的超高维度和高度相关的特征空间的错误控制功能选择
Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning
论文作者
论文摘要
近年来，由于其在分析复杂数据对象方面的令人印象深刻的经验成功，深度学习一直处于分析的中心。尽管取得了成功，但大多数现有工具都像黑盒机器一样行事，因此，对可解释，可靠和健壮的深度学习模型的兴趣日益增加，适用于广泛的应用程序。特征选择的深度学习已成为该领域中有前途的工具。但是，除了高噪声水平外，最近的发展不适合超高维度和高度相关的特征。在本文中，我们提出了一种新颖的筛查和清洁方法，借助深度学习，以对具有控制错误率的高度相关预测变量进行数据自适应的多分辨率发现。在广泛的模拟场景和几个真实数据集中进行了广泛的经验评估，证明了该方法在实现高功率的同时将虚假发现率最少保持最低限度的有效性。
In recent years, deep learning has been at the center of analytics due to its impressive empirical success in analyzing complex data objects. Despite this success, most of the existing tools behave like black-box machines, thus the increasing interest in interpretable, reliable, and robust deep learning models applicable to a broad class of applications. Feature-selected deep learning has emerged as a promising tool in this realm. However, the recent developments do not accommodate ultra-high dimensional and highly correlated features, in addition to the high noise level. In this article, we propose a novel screening and cleaning method with the aid of deep learning for a data-adaptive multi-resolutional discovery of highly correlated predictors with a controlled error rate. Extensive empirical evaluations over a wide range of simulated scenarios and several real datasets demonstrate the effectiveness of the proposed method in achieving high power while keeping the false discovery rate at a minimum.
