标题： 基于模型的提升方法用于稀疏快速变量选择 显示英文标题

标题： Probing for sparse and fast variable selection with model-based boosting

摘要： 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同时进行变量选择的同时拟合统计模型的工具。 拟合的一个缺点在于需要在略微改变的数据上进行多次模型拟合（例如，交叉验证或自助法）以找到最佳的提升迭代次数并防止过拟合。 在我们提出的方法中，我们将数据集与真实变量的随机排列版本（称为影子变量）相结合，并在这样的变量将被添加到模型中时立即停止逐步拟合。 这允许在一次模型拟合中进行变量选择，而无需进一步的参数调整。 我们表明，我们的探测方法在高维分类基准中可以与最先进的选择方法如稳定性选择相媲美，并将其应用于基因表达数据以估计枯草芽孢杆菌的核黄素产量。 显示英文摘要

摘要： 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.