论文标题
稀疏组提升 - 无偏组和可变选择
Sparse-group boosting -- Unbiased group and variable selection
论文作者
论文摘要
在存在分组的协变量的情况下，我们提出了一个提升框架，以允许在组内和组之间实现稀疏性。通过使用调整后的自由度同时使用组件和小组梯度提升，可以通过增强来拟合具有与稀疏组套索相似的模型。我们表明，组内和组间稀疏性可以通过混合参数来控制，并讨论稀疏组套索中混合参数的相似性和差异。借助模拟，基因数据以及农业数据，我们显示了该估计器的有效性和预测性竞争力。数据和仿真表明，在存在分组变量的情况下，稀疏组提升的使用与偏差的变量选择较少，并且与组件的增强相比，可预测性较小。此外，我们提出了一种通过自由度来促进组成部分的偏见的方法。
In the presence of grouped covariates, we propose a framework for boosting that allows to enforce sparsity within and between groups. By using component-wise and group-wise gradient boosting at the same time with adjusted degrees of freedom, a model with similar properties as the sparse group lasso can be fitted through boosting. We show that within-group and between-group sparsity can be controlled by a mixing parameter and discuss similarities and differences to the mixing parameter in the sparse group lasso. With simulations, gene data as well as agricultural data we show the effectiveness and predictive competitiveness of this estimator. The data and simulations suggest, that in the presence of grouped variables the use of sparse group boosting is associated with less biased variable selection and higher predictability compared to component-wise boosting. Additionally, we propose a way of reducing bias in component-wise boosting through the degrees of freedom.
