[1]张莹,毕卓. 基于SPMD的C4.5并行决策树加速分析[J].计算机技术与发展,2015,25(01):29-32.
　ZHANG Ying,BI Zhuo. Analysis of Parallel C4 . 5 Decision Tree Acceleration Based on SPMD[J].,2015,25(01):29-32.
点击复制

 基于SPMD的C4.5并行决策树加速分析()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年01期
页码:
29-32
栏目:
智能、算法、系统工程
出版日期:
2015-01-10

文章信息/Info

Title:
 Analysis of Parallel C4 . 5 Decision Tree Acceleration Based on SPMD
文章编号:
1673-629X（2015)01-0029-04
作者:
 张莹; 毕卓
 上海大学 机电工程与自动化学院
Author(s):
 ZHANG Ying; BI Zhuo
关键词:
 ; C4. 5; 并行; 决策树; MATLAB
Keywords:
 ; C4. 5; parallel; decision tree; MATLAB
分类号:
TP301
文献标志码:
A
摘要:
 决策树分类方法是解决数据挖掘、模式识别中分类任务的有效方法，然而，在大规模的数据集上运行时，其运行效率受到严重影响。文中选取决策树的代表算法C4.5算法为研究对象，利用算法固有的并行性对其进行优化研究。文中利用MATLAB实现串行的C4.5决策树，并对构成该决策树的子函数进行运行时间分析，从而确定信息增益率计算的复杂性为限制算法速度的关键因素。针对此计算瓶颈，结合决策树算法在子节点分割以及最优分裂属性选择等方面的并行性，纵向划分数据，构建了并行的C4.5决策树，并利用MATLAB并行计算池功能以及SPMD设计实现。对并行后决策树运行时间验证结果表明，将C4.5决策树并行化后，并行决策树的构建时间显著缩短，实现了算法的加速。
Abstract:
 Decision tree is an effective classification algorithm in data mining and pattern recognition. However,its performance is largely affected with size increasing of data sets. It selects the C4. 5,the representative decision tree algorithm,as the research object,using the in-herent parallelization of the algorithm for optimization study. Serial C4. 5 decision tree algorithm is first implemented on MATLAB and the running time of the sub-functions is analyzed so as to find out the bottleneck of the algorithm. To solve the problem of spending too much time on computing information gain ratio,a parallel C4. 5 decision is established with longitudinal partition of data in selecting the best spit attribute,and design and realize it by MATLAB parallel computing pool function and SPMD. Test results for decision tree run-ning time show that generation time of parallel decision tree can be largely reduced after parallelizing the C4. 5 decision tree,realizing the algorithm acceleration.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(01):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(01):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(01):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(01):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(01):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(01):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(01):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(01):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(01):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(01):47.

更新日期/Last Update: 2015-04-17