# Model-free Feature Screening and FDR Control with Knockoff Features

arXiv: https://arxiv.org/abs/1908.06597 · HTML: https://mlsanity.com/paper/1908.06597/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [A model-free feature selection technique of feature screening and random forest based recursive feature elimination](https://mlsanity.com/paper/2302.07449/) (2023)
- [Error-based Knockoffs Inference for Controlled Feature Selection](https://mlsanity.com/paper/2203.04483/) (2022)
- [Fr'echet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects](https://mlsanity.com/paper/2502.15374/) (2025)
- [Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm](https://mlsanity.com/paper/2401.08150/) (2024)
- [Sparse minimum Redundancy Maximum Relevance for feature selection](https://mlsanity.com/paper/2508.18901/) (2025)
- [Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data](https://mlsanity.com/paper/2509.02648/) (2025)
- [An adaptive subsampling method for large-sample feature screening](https://mlsanity.com/paper/2509.16085/) (2025)
- [A Fast Screening Approach for High-dimensional Outcomes and High-dimensional Predictors](https://mlsanity.com/paper/2606.03018/) (2026)
- [PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models](https://mlsanity.com/paper/2609.23574/) (2026)

## Abstract (arXiv)

This paper proposes a model-free and data-adaptive feature screening method for ultra-high dimensional datasets. The proposed method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model and applies to the data in the presence of heavy-tailed errors and multivariate response. It enjoys both sure screening and rank consistency properties under weak assumptions. Further, a two-step approach is proposed to control the false discovery rate (FDR) in feature screening with the help of knockoff features. It can be shown that the proposed two-step approach enjoys both sure screening and FDR control if the pre-specified FDR level α is greater or equal to 1/s, where s is the number of active features. The superior empirical performance of the proposed methods is justified by various numerical experiments and real data applications.