Error-Based Knockoffs Inference for Controlled Feature Selection

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.

Authors

• Xuebin Zhao
• Hong Chen
• Yingjie Wang College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
• Weifu Li College of Informatics, Huazhong Agricultural University, Wuhan, China Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, China Key Laboratory of Smart Farming for Agricultural Animals, Wuhan, China
• Tieliang Gong School of Computer Science and Technology, MOEKLINNS, Xi’an Jiaotong University
• Yulong Wang College of Computer Science, Nankai University Tianjin Media Computing Center
• Feng Zheng Southern University of Science and Technology

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