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：本文提出数据流算法，用于计算决策树学习中的最优分裂点。具体而言，给定观测值 $x_i$ 及其标签 $y_i$ 构成的数据流，目标是找到最优分裂点 $j$，将该数据划分为两个集合，使得均方误差（回归任务）或误分类率（分类任务）最小化。我们针对这些问题提出了多种快速流式算法，这些算法使用亚线性空间且仅需少量遍历。这些算法还可拓展至大规模并行计算模型。尽管无法直接比较，本研究对 Domingos 与 Hulten（KDD 2000）的开创性工作形成了重要补充。