A novel bagging approach for variable ranking and selection via a mixed importance measure-Reference-Cited by-同舟云学术

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

Published:2017-10-23 Issue:10 Volume:45 Page:1734-1755
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Zhang Chun-Xia¹,Zhang Jiang-She¹,Wang Guan-Wei²,Ji Nan-Nan³

Affiliation:

1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an Shaanxi, People's Republic of China

2. School of Mechatronic Engineering and Shaanxi Key Laboratory of Non-Traditional Machining, Xi'an Technological University, Xi'an Shaanxi, People's Republic of China

3. School of Science, Chang'an University, Xi'an Shaanxi, People's Republic of China

Funder

National Natural Science Foundation of China

Program for International Science and Technology Cooperation Projects of China

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2017.1391181

Reference38 articles.

1. F.R. Bach, Bolasso: Model consistent lasso estimation through the bootsrap, Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, ACM Press, 2008, pp. 33–40.

2. Heuristics of instability and stabilization in model selection

3. Bagging predictors

4. Stochastic correlation coefficient ensembles for variable selection

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