论文标题
拆分：数据拆分的最佳方法
SPlit: An Optimal Method for Data Splitting
论文作者
论文摘要
在本文中，我们提出了一种最佳方法，称为将数据集拆分为培训和测试集的拆分。拆分基于支持点（SP）的方法，该方法最初是为了找到连续分布的最佳代表点而开发的。我们使用顺序最近的邻居算法从数据集中调整SP。我们还扩展了SP来处理分类变量，因此可以将拆分应用于回归和分类问题。与常用的随机分裂过程相比，在实际数据集上的拆分实现在最差的案例测试性能方面显示出很大的改善。
In this article we propose an optimal method referred to as SPlit for splitting a dataset into training and testing sets. SPlit is based on the method of Support Points (SP), which was initially developed for finding the optimal representative points of a continuous distribution. We adapt SP for subsampling from a dataset using a sequential nearest neighbor algorithm. We also extend SP to deal with categorical variables so that SPlit can be applied to both regression and classification problems. The implementation of SPlit on real datasets shows substantial improvement in the worst-case testing performance for several modeling methods compared to the commonly used random splitting procedure.
