A novel bagging approach for variable ranking and selection via a mixed importance measure

DOI10.1080/02664763.2017.1391181OpenAlexW2765361488MaRDI QIDQ5036437

Guan-Wei Wang, Jiang-She Zhang, Chun-Xia Zhang, Nannan Ji

Publication date: 23 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2017.1391181

zbMATH Keywords

variable selectionbaggingensemble learningvariable rankingvariable selection ensembleselection accuracystepwise search algorithm

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05) Statistical ranking and selection procedures (62F07) Applications of statistics (62Pxx)

Uses Software

• UCI-ml
• AdaBoost.MH
• PBoostGA
• dobson
• Bolasso
• RandGA

Cites Work

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• Bagging predictors
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• Least angle regression. (With discussion)
• Ensemble approaches for regression
• Combining Pattern Classifiers
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• On stability issues in deriving multivariable regression models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Stability Selection
• Variable Selection with Error Control: Another Look at Stability Selection
• RandGA: injecting randomness into parallel genetic algorithm for variable selection
• Stochastic correlation coefficient ensembles for variable selection
• Variable selection for sparse high-dimensional nonlinear regression models by combining nonnegative garrote and sure independence screening
• Variable selection by ensembles for the Cox model
• Regularization and Variable Selection Via the Elastic Net

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