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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 1421409, 8 pages
Research Article

Probing for Sparse and Fast Variable Selection with Model-Based Boosting

1Department of Statistics, LMU München, München, Germany
2Department of Medical Informatics, Biometry and Epidemiology, FAU Erlangen-Nürnberg, Erlangen, Germany
3Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany

Correspondence should be addressed to Tobias Hepp; ed.negnalre-ku@ppeh.saibot

Received 9 February 2017; Accepted 13 April 2017; Published 31 July 2017

Academic Editor: Yuhai Zhao

Copyright © 2017 Janek Thomas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.