Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection
This work addresses variable selection challenges for researchers and practitioners dealing with correlated high-dimensional data, representing an incremental improvement over existing Knockoff methods.
The paper tackles the problem of variable selection in high-dimensional datasets by introducing Knoop, a method that enhances Knockoff filters through over-parameterization, achieving higher AUC in simulations and improved predictive accuracy in real-world tasks.
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.