Probing for sparse and fast variable selection with model-based boosting
This work addresses the computational inefficiency of variable selection for researchers using model-based boosting, though it is incremental as it builds on existing boosting techniques.
The authors tackled the problem of variable selection in model-based boosting by introducing a probing method that uses randomly permuted shadow variables to stop fitting automatically, eliminating the need for multiple model fits and parameter tuning. They demonstrated that this approach competes with state-of-the-art methods like stability selection in high-dimensional classification benchmarks and applied it to gene expression data for riboflavin production estimation.
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 step-wise 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 gene expression data for the estimation of riboflavin production of Bacillus subtilis.