论文标题
JSRT：James-Stein回归树
JSRT: James-Stein Regression Tree
论文作者
论文摘要
回归树（RT）已被广泛用于机器学习和数据挖掘社区。给定预测的目标数据，在对每个叶子节点进行预测之前，首先是基于训练数据集构建回归树。实际上，RT的性能在很大程度上依赖于树木构建/预测阶段中单个节点的样本的本地平均值，同时忽略了来自不同节点的全球信息，这也起着重要作用。为了解决这个问题，我们通过考虑来自不同节点的全局信息，提出了一棵新颖的回归树，名为James-Stein回归树（JSRT）。具体而言，我们根据施工/预测阶段的不同节点的James-Stein估计器结合了全球平均信息。此外，我们在均方误差（MSE）度量下分析了我们方法的概括误差。公共基准数据集的广泛实验验证了我们方法的有效性和效率，并证明了我们方法比其他RT预测方法的优越性。
Regression tree (RT) has been widely used in machine learning and data mining community. Given a target data for prediction, a regression tree is first constructed based on a training dataset before making prediction for each leaf node. In practice, the performance of RT relies heavily on the local mean of samples from an individual node during the tree construction/prediction stage, while neglecting the global information from different nodes, which also plays an important role. To address this issue, we propose a novel regression tree, named James-Stein Regression Tree (JSRT) by considering global information from different nodes. Specifically, we incorporate the global mean information based on James-Stein estimator from different nodes during the construction/predicton stage. Besides, we analyze the generalization error of our method under the mean square error (MSE) metric. Extensive experiments on public benchmark datasets verify the effectiveness and efficiency of our method, and demonstrate the superiority of our method over other RT prediction methods.
