通过过参数化增强敲除法，更准识别重要变量
Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection
- 用多个敲除变量融合原变量，构建过参数化模型
- 通过系数分布对比实现异常检测，显著提升AUC
- 适合高维相关数据的变量选择，尤其在真实数据上表现强
变量选择在诸多领域对提升建模效果至关重要，尤其面对高维相关变量数据时。本文提出一种新方法Knoop，通过过参数化增强敲除法（Knockoff）进行变量选择。具体地，Knoop为每个原始变量生成多个敲除变量，并将其与原始变量一同输入过参数化无正则岭回归模型。针对每个原始变量，Knoop评估其敲除变量的系数分布，并与原始系数比较，实施基于异常的显著性检验，确保变量选择稳健。大量实验表明，该方法在模拟和真实数据集上均优于现有方法：在受控模拟中，识别相关变量的接收者操作特征曲线下面积（AUC）显著更高；在各类回归与分类任务中，预测精度也明显提升。理论分析进一步支持了这些观察结果。
原文摘要 · Abstract (English)
Variable selection plays a crucial role in enhancing modeling effectiveness across diverse fields, addressing the challenges posed by high-dimensional datasets of correlated variables. This work introduces a novel approach namely Knockoff with over-parameterization (Knoop) to enhance Knockoff filters for variable selection. Specifically, Knoop first generates multiple knockoff variables for each original variable and integrates them with the original variables into an over-parameterized Ridgeless regression model. For each original variable, Knoop evaluates the coefficient distribution of its knockoffs and compares these with the original coefficients to conduct an anomaly-based significance test, ensuring robust variable selection. Extensive experiments demonstrate superior performance compared to existing methods in both simulation and real-world datasets. Knoop achieves a notably higher Area under the Curve (AUC) of the Receiver Operating Characteristic (ROC) Curve for effectively identifying relevant variables against the ground truth by controlled simulations, while showcasing enhanced predictive accuracy across diverse regression and classification tasks. The analytical results further backup our observations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。