JSRT: James-Stein Regression Tree

• URL: http://arxiv.org/abs/2010.09022v2
• Date: Wed, 21 Oct 2020 13:26:26 GMT
• Title: JSRT: James-Stein Regression Tree
• Authors: Xingchun Xiang, Qingtao Tang, Huaixuan Zhang, Tao Dai, Jiawei Li, Shu-Tao Xia
• Abstract summary: Regression tree (RT) has been widely used in machine learning and data mining community. In practice, the performance of RT relies heavily on the local mean of samples from an individual node during the tree construction/prediction stage. We propose a novel regression tree, named James-Stein Regression Tree (JSRT) by considering global information from different nodes.
• Score: 55.2059664267247
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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