Error-Based Knockoffs Inference for Controlled Feature Selection

Xuebin Zhao1; Hong Chen1; Yingjie Wang2; Weifu Li1; Tieliang Gong3; Yulong Wang2; Feng Zheng4

2022

36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

2159-5399

2374-3468

THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE

FEB 22-MAR 01, 2022

null,null,ELECTR NETWORK

2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE

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.

Others

English

CPCI-S

National Natural Science Foundation of China["12071166","62076041","61702057","61806027","61972188","62106191"] ; Fundamental Research Funds for the Central Universities of China[2662020LXQD002]

Computer Science

Computer Science, Artificial Intelligence

WOS:000893639102024

Web of Science

1.College of Science, Huazhong Agricultural University, Wuhan 430062, China
2.College of Informatics, Huazhong Agricultural University, Wuhan 430062, China
3.School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
4.Southern University of Science and Technology, China

Xuebin Zhao,Hong Chen,Yingjie Wang,et al. Error-Based Knockoffs Inference for Controlled Feature Selection[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2022.