Error-based Knockoffs Inference for Controlled Feature Selection
- URL: http://arxiv.org/abs/2203.04483v1
- Date: Wed, 9 Mar 2022 01:55:59 GMT
- Title: Error-based Knockoffs Inference for Controlled Feature Selection
- Authors: Xuebin Zhao, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong, Yulong
Wang, Feng Zheng
- Abstract summary: We propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together.
The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees.
- Score: 49.99321384855201
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recently, the scheme of model-X knockoffs was proposed as a promising
solution to address controlled feature selection under high-dimensional
finite-sample settings. However, the procedure of model-X knockoffs depends
heavily on the coefficient-based feature importance and only concerns the
control of false discovery rate (FDR). To further improve its adaptivity and
flexibility, in this paper, we propose an error-based knockoff inference method
by integrating the knockoff features, the error-based feature importance
statistics, and the stepdown procedure together. The proposed inference
procedure does not require specifying a regression model and can handle feature
selection with theoretical guarantees on controlling false discovery proportion
(FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations
demonstrate the competitive performance of our approach on both simulated and
real data.
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