统计Boosting中基础学习者的取消选择--及其在分布回归中的应用
Deselection of Base-Learners for Statistical Boosting -- with an Application to Distributional Regression
摘要：
我们提出了一个新的程序，增强的变量选择组件的梯度提升。统计提升是从机器学习中出现的一种计算方法，它允许在存在高维数据的情况下拟合回归模型。此外，该算法可以导致数据驱动的变量选择。然而，在实践中，最终模型通常倾向于在某些情况下包括太多的变量。这种情况尤其发生在低维数据（p<n）中，我们观察到一种缓慢的过拟合行为。因此，更多的变量被纳入最终模型，而不会改变预测精度。这些假阳性中的许多与小系数合并，因此具有小的影响，但导致更大的模型。我们试图克服这个问题，给算法的机会，学习者的重要性较低。我们分析了新方法对变量选择和预测性能的影响，与其他方法相比，包括提前停止的提升以及双提升。我们用一项正在进行的慢性肾脏病患者队列研究的数据来说明我们的方法，其中最有影响力的健康相关生活质量指标的预测因子是在基于β回归的分布回归方法中选择的。
英文摘要：
We present a new procedure for enhanced variable selection for component-wise gradient boosting. Statistical boosting is a computational approach that emerged from machine learning, which allows to fit regression models in the presence of high-dimensional data. Furthermore, the algorithm can lead to data-driven variable selection. In practice, however, the final models typically tend to include too many variables in some situations. This occurs particularly for low-dimensional data (p<n), where we observe a slow overfitting behavior of boosting. As a result, more variables get included into the final model without altering the prediction accuracy. Many of these false positives are incorporated with a small coefficient and therefore have a small impact, but lead to a larger model. We try to overcome this issue by giving the algorithm the chance to deselect base-learners with minor importance. We analyze the impact of the new approach on variable selection and prediction performance in comparison to alternative methods including boosting with earlier stopping as well as twin boosting. We illustrate our approach with data of an ongoing cohort study for chronic kidney disease patients, where the most influential predictors for the health-related quality of life measure are selected in a distributional regression approach based on beta regression.
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