  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.
