论文标题
随机空间森林
Random Spatial Forests
论文作者
论文摘要
我们介绍了随机的空间森林，一种装袋回归树的方法，允许空间相关。我们的主要贡献是开发一种计算高效的树木建筑算法，该算法选择了树的每个拆分以进行空间相关性。我们评估了两种不同的方法来估计随机空间森林，这是一种伪样方法，将随机森林与Kriging结合在一起，并为一般的空间Smoothorts类别提供了非参数版本。与现有的两步方法相比，我们显示了提高方法的预测准确性，这些方法与一系列数值模拟结合了随机森林和kriging，并证明了其在2009年至2010年从美国大陆的元素碳，有机碳，硅和硫的测量结果上的性能。
We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient tree building algorithm which selects each split of the tree adjusting for spatial correlation. We evaluate two different approaches for estimation of random spatial forests, a pseudo-likelihood approach combining random forests with kriging and a non-parametric version for a general class of spatial smoothers. We show improved prediction accuracy of our method compared to existing two-step approaches combining random forests and kriging across a range of numerical simulations and demonstrate its performance on elemental carbon, organic carbon, silicon, and sulfur measurements across the continental United States from 2009-2010.
