Thomas Janek, Hepp Tobias, Mayr Andreas, Bischl Bernd
Department of Statistics, LMU München, München, Germany.
Department of Medical Informatics, Biometry and Epidemiology, FAU Erlangen-Nürnberg, Erlangen, Germany.
Comput Math Methods Med. 2017;2017:1421409. doi: 10.1155/2017/1421409. Epub 2017 Jul 31.
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.
我们提出了一种基于模型的梯度提升和随机排列变量的新变量选择方法。基于模型的提升是一种在执行变量选择的同时拟合统计模型的工具。这种拟合的一个缺点在于需要在略有改变的数据上进行多次模型拟合（例如交叉验证或自助法），以找到最优的提升迭代次数并防止过拟合。在我们提出的方法中，我们用真实变量的随机排列版本（即所谓的影子变量）扩充数据集，并在将这样一个变量添加到模型时立即停止逐步拟合。这使得在单次模型拟合中就能进行变量选择，而无需进一步的参数调整。我们表明，在高维分类基准测试中，我们的探测方法可以与诸如稳定性选择等最先进的选择方法相媲美，并将其应用于三个基因表达数据集。
