An Ensemble Method for Feature Screening

Xi Wu, Shifeng Xiong and Weiyan Mu ()
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Xi Wu: National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100021, China
Shifeng Xiong: NCMIS, KLSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Weiyan Mu: School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Mathematics, 2023, vol. 11, issue 2, 1-14

Abstract: It is known that feature selection/screening for high-dimensional nonparametric models is an important but very difficult issue. In this paper, we first point out the limitations of existing screening methods. In particular, model-free sure independence screening methods, which are defined on random predictors, may completely miss some important features in the underlying nonparametric function when the predictors follow certain distributions. To overcome these limitations, we propose an ensemble screening procedure for nonparametric models. It elaborately combines several existing screening methods and outputs a result close to the best one of these methods. Numerical examples indicate that the proposed method is very competitive and has satisfactory performance even when existing methods fail.

Keywords: classification; nonparametric regression; R-square; sparsity; sure independent screening; variable selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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