scholarly journals A class comparison method with filtering-enhanced variable selection for high-dimensional data sets

2008 ◽  
Vol 27 (28) ◽  
pp. 5834-5849 ◽  
Author(s):  
Lara Lusa ◽  
Edward L. Korn ◽  
Lisa M. McShane
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Comparison Method ◽  
High Dimensional ◽  
Data Sets ◽  
Class Comparison ◽  
Selection For

Sparse Bayesian variable selection for classifying high-dimensional data

2018 ◽  
Vol 11 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Aijun Yang ◽  
Heng Lian ◽  
Xuejun Jiang ◽  
Pengfei Liu
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Bayesian Variable Selection ◽  
High Dimensional ◽  
Selection For

LASSO-Type Variable Selection Methods for High-Dimensional Data

2013 ◽  
Vol 444-445 ◽  
pp. 604-609
Author(s):  
Guang Hui Fu ◽  
Pan Wang
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Real Data ◽  
Oracle Property ◽  
High Dimensional ◽  
Data Sets ◽  
Group Effect ◽  
Selection Methods ◽  
Variable Selection Method ◽  
Type Variable

LASSO is a very useful variable selection method for high-dimensional data , But it does not possess oracle property [Fan and Li, 200 and group effect [Zou and Hastie, 200. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and (or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.

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