In this work, we provide data stream algorithms that compute optimal splits in decision tree learning. In particular, given a data stream of observations $x_i$ and their labels $y_i$, the goal is to find the optimal split point $j$ that divides the data into two sets such that the mean squared error (for regression) or misclassification rate (for classification) is minimized. We provide various fast streaming algorithms that use sublinear space and a small number of passes for these problems. These algorithms can also be extended to the massively parallel computation model. Our work, while not directly comparable, complements the seminal work of Domingos and Hulten (KDD 2000).

翻译：本文提出用于决策树学习中计算最优分裂点的数据流算法。具体而言，给定由观测值$x_i$及其标签$y_i$构成的数据流，目标在于寻找最优分裂点$j$，使得该点将数据划分为两个子集后，均方误差（回归任务）或误分类率（分类任务）最小化。针对此类问题，我们设计了多种采用亚线性空间与少量遍历次数的快速流式算法，并能将其扩展至大规模并行计算模型。本研究虽无法直接比较，但对Domingos与Hulten（KDD 2000）的开创性工作形成了有益补充。