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Subsampling Winner Algorithm 2 for Feature Selection from Large Data

Abstract Number:

3385 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Wei Dai (1), Jiayang Sun (1), YIYING Fan (2)

Institutions:

(1) George Mason University, N/A, (2) Cleveland State University, N/A

Co-Author(s):

Jiayang Sun  
George Mason University
YIYING Fan  
Cleveland State University

First Author:

Wei Dai  
George Mason University

Presenting Author:

Wei Dai  
George Mason University

Abstract Text:

The Subsampling Winner Algorithm (SWA, Fan and Sun 2021) provides a novel alternative to penalized methods and random forest procedures. This paper introduces SWA2, the next generation of SWA designed for more general data that may be subject to data heteroskedasticity or interactions. The performance of SWA2 is compared with benchmark methods, including LASSO and SCAD. A new, faster algorithm will be demonstrated through examples.

Keywords:

feature selection|ensemble method|regression|heteroskedasticity| high dimensional data|

Sponsors:

Section on Statistical Computing

Tracks:

Variable Selection

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