论文标题
神经高斯镜，用于神经网络中的受控特征选择
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
论文作者
论文摘要
深度神经网络（DNN）在预测任务中变得越来越流行，并取得了出色的表现。但是，DNN框架本身无法告知用户哪些功能或多或少与进行预测相关，从而限制了其在许多科学领域的适用性。我们介绍了神经高斯镜（NGMS），其中通过基于基于内核的条件依赖度量的结构化扰动创建了镜像特征，以帮助评估特征的重要性。我们设计了DNN体系结构的两种修改，用于结合镜像功能并提供镜像统计信息以衡量特征重要性。如模拟和真实数据示例所示，所提出的方法以预定义的级别控制特征选择错误率，即使存在高度相关的特征，也可以保持高选择功率。
Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user which features are more or less relevant for making the prediction, which limits its applicability in many scientific fields. We introduce neural Gaussian mirrors (NGMs), in which mirrored features are created, via a structured perturbation based on a kernel-based conditional dependence measure, to help evaluate feature importance. We design two modifications of the DNN architecture for incorporating mirrored features and providing mirror statistics to measure feature importance. As shown in simulated and real data examples, the proposed method controls the feature selection error rate at a predefined level and maintains a high selection power even with the presence of highly correlated features.
