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计算机系统应用英文版:2021,30(2):201-206
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基于ELM的改进CART决策树回归算法
(东北石油大学 计算机与信息技术学院, 大庆 163318)
Improved CART Decision Tree Regression Algorithm Based on ELM
(School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China)
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Received:June 23, 2020    Revised:July 14, 2020
中文摘要: 为提高CART (Classification And Regression Tree)决策树回归算法的准确性, 提出一种基于ELM (Extreme Learning Machine)的改进CART决策树回归算法——ELM-CART算法. 所提算法主要是在CART回归树创建过程中, 在每个叶节点使用极限学习机建模, 可以得到真正意义上的回归预测值, 提高泛化能力, 弥补CART决策树回归算法本身的容易过拟合以及预测输出为定值等缺点. 实验结果表明, 所提算法能够有效提高回归分析中目标数据的预测准确性, 其准确性优于所对比算法.
Abstract:In order to increase the accuracy of the Classification And Regression Tree (CART) regression algorithm, we propose an improved CART regression algorithm based on Extreme Learning Machine (ELM-CART for short). The proposed algorithm mainly applies the ELM for modeling at each leaf node in the process of creating a CART, which can get the true regression prediction value, improve the generalization ability, and compensate for such disadvantages of the CART regression algorithm as easy overfitting and constant predictive output. The experimental results show that the proposed algorithm can effectively improve the prediction accuracy of target data in regression analysis, and its accuracy is higher than that of the counterparts.
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基金项目:国家自然科学基金(617020936); 大庆市指导性科技项目(zd-2019-09)
引用文本:
王宏,张强,王颖,郭玉洁.基于ELM的改进CART决策树回归算法.计算机系统应用,2021,30(2):201-206
WANG Hong,ZHANG Qiang,WANG Ying,GUO Yu-Jie.Improved CART Decision Tree Regression Algorithm Based on ELM.COMPUTER SYSTEMS APPLICATIONS,2021,30(2):201-206