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Probing for Sparse and Fast Variable Selection with Model-Based Boosting
Joint Authors
Thomas, Janek
Hepp, Tobias
Mayr, Andreas
Bischl, Bernd
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-31
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables.
Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time.
A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting.
In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model.
This allows variable selection in a single fit of the model without requiring further parameter tuning.
We show that our probing approach can compete with state-of-the-art selection methods like stability selection in a high-dimensional classification benchmark and apply it on three gene expression data sets.
American Psychological Association (APA)
Thomas, Janek& Hepp, Tobias& Mayr, Andreas& Bischl, Bernd. 2017. Probing for Sparse and Fast Variable Selection with Model-Based Boosting. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1141943
Modern Language Association (MLA)
Thomas, Janek…[et al.]. Probing for Sparse and Fast Variable Selection with Model-Based Boosting. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1141943
American Medical Association (AMA)
Thomas, Janek& Hepp, Tobias& Mayr, Andreas& Bischl, Bernd. Probing for Sparse and Fast Variable Selection with Model-Based Boosting. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1141943
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141943