DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection

arXiv:2402.17176v1 Announce Type: new
Abstract: Model-X knockoff, among various feature selection methods, received much attention recently due to its guarantee on false discovery rate (FDR) control. Subsequent to its introduction in parametric design, knockoff is advanced to handle arbitrary data distributions using deep learning-based generative modeling. However, we observed that current implementations of the deep Model-X knockoff framework exhibit limitations. Notably, the "swap property" that knockoffs necessitate frequently encounter challenges on sample level, leading to a diminished selection power. To …

abstract advanced arxiv attention control cs.lg current data deep learning design discovery false fdr feature feature selection generative generative modeling introduction modeling parametric rate type