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

Thomas J, Hepp T, Mayr A, Bischl B (2017)

Publication Type: Journal article

Publication year: 2017

Journal

Computational and Mathematical Methods in Medicine Taylor & Francis: STM, Behavioural Science and Public Health Titles / Taylor & Francis / Hindawi Publishing Corporation

Book Volume: 2017

DOI: 10.1155/2017/1421409

Abstract

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.

Authors with CRIS profile

Tobias Hepp Lehrstuhl für Biometrie und Epidemiologie (IMBE) Andreas Mayr Lehrstuhl für Biometrie und Epidemiologie (IMBE)

Involved external institutions

Ludwig-Maximilians-Universität (LMU) DE Germany (DE)

How to cite

APA:

Thomas, J., Hepp, T., Mayr, A., & Bischl, B. (2017). Probing for Sparse and Fast Variable Selection with Model-Based Boosting. Computational and Mathematical Methods in Medicine, 2017. https://doi.org/10.1155/2017/1421409

MLA:

Thomas, Janek, et al. "Probing for Sparse and Fast Variable Selection with Model-Based Boosting." Computational and Mathematical Methods in Medicine 2017 (2017).

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