南方科技大学知识苑(SUSTech KC): Error-Based Knockoffs Inference for Controlled Feature Selection

题名	Error-Based Knockoffs Inference for Controlled Feature Selection
作者	Xuebin Zhao 1; Hong Chen 1; Yingjie Wang 2; Weifu Li 1; Tieliang Gong 3; Yulong Wang 2; Feng Zheng4
通讯作者	Hong Chen
发表日期	2022
会议名称	36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence
ISSN	2159-5399
EISSN	2374-3468
会议录名称	THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
会议日期	FEB 22-MAR 01, 2022
会议地点	null,null,ELECTR NETWORK
出版地	2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
出版者	ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
摘要	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.
学校署名	其他
语种	英语
相关链接	[来源记录]
收录类别	CPCI-S
资助项目	National Natural Science Foundation of China["12071166","62076041","61702057","61806027","61972188","62106191"] ; Fundamental Research Funds for the Central Universities of China[2662020LXQD002]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000893639102024
来源库	Web of Science
引用统计	被引频次[WOS]：1
成果类型	会议论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/415789
专题	南方科技大学工学院_计算机科学与工程系
作者单位	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
推荐引用方式 GB/T 7714	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.