个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:东北大学
学位:博士
所在单位:控制科学与工程学院
学科:应用数学. 应用数学. 控制理论与控制工程
办公地点:创新园大厦A0620
联系方式:电话: (+86-411) 84726020 (home) (+86-411) 84709380 (Office) 传真: (+86-411) 84707579 手机: (+86-411) 13130042458
电子邮箱:xdliuros@dlut.edu.cn
A Pearson's correlation coefficient based decision tree and its parallel implementation
点击次数:
论文类型:期刊论文
发表时间:2018-04-01
发表刊物:INFORMATION SCIENCES
收录刊物:SCIE、EI
卷号:435
页面范围:40-58
ISSN号:0020-0255
关键字:Decision trees; Parallel computing; Pearson's correlation coefficient; Map-Reduce
摘要:In this paper, a Pearson's correlation coefficient based decision tree (PCC-Tree) is established and its parallel implementation is developed in the framework of Map-Reduce (MR-PCC-Tree). The proposed methods employ Pearson's correlation coefficient as a new measure of feature quality to confirm the optimal splitting attributes and splitting points in the growth of decision trees. Besides, the proposed MR-PCC-Tree adopts Map-Reduce technology to every component during the decision trees learning process for parallel computing, which mainly consists of a parallel Pearson's correlation coefficient based splitting rule and a parallel splitting data method. The experimental analysis is conducted on a series of UCI benchmark data sets with different scales. In contrast to several traditional decision tree classifiers including BFT, C4.5, LAD, SC and NBT on 17 data sets, the proposed PCC-Tree is no worse than the traditional models as a whole. Furthermore, the experimental results on other 8 data sets show the feasibility of the proposed MR-PCC-Tree and its good parallel performance on reducing computational time for large-scale data classification problems. (C) 2017 Elsevier Inc. All rights reserved.