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作者:

Yuan, F. (Yuan, F..) | Lian, F. (Lian, F..) | Xu, X. (Xu, X..) | Ji, Z. (Ji, Z..)

收录:

Scopus

摘要:

With the advent of the computer science, the data volume that needed to be processed under many practical situations increases dramatically, challenging many traditional machine learning techniques. Bearing this in mind, we made an intensive study on the optimization of decision tree algorithm and its corresponding porting to the big data analysis in this paper. An optimized genetic algorithm is merged into the implementation of the decision tree algorithm above, and we also invent a parallel genetic decision tree algorithm using MapReduce, which is very suitable for analyzing big data in cloud computing environment. Experiment results show that our algorithm acquires a nearly linear speedup, keeping a similar classification accuracy at the same time. © 2015 IEEE.

关键词:

cloud computing; data mining; decision tree; genetic algorithm; mapreduce

作者机构:

  • [ 1 ] [Yuan, F.]College of Computer and Information Engineering, Inner Mongolia Normal University, Hohhot, 010010, China
  • [ 2 ] [Yuan, F.]College of Computer and Information Engineering, Inner Mongolia Normal University, China
  • [ 3 ] [Lian, F.]Xing An Vocational and Technical College, Wulanhaote, 137400, China
  • [ 4 ] [Xu, X.]CAS Key Laboratory of Genome Sciences and Information Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
  • [ 5 ] [Xu, X.]University of Chinese Academy of Sciences, Beijing, 100049, China
  • [ 6 ] [Ji, Z.]College of Computer and Information Engineering, Inner Mongolia Normal University, Hohhot, 010010, China
  • [ 7 ] [Ji, Z.]College of Environment and Energy Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 8 ] [Ji, Z.]College of Network Technology, Inner Mongolia Normal University, Hohhot, 010010, China

通讯作者信息:

  • [Ji, Z.]College of Computer and Information Engineering, Inner Mongolia Normal UniversityChina

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来源 :

Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS

ISSN: 2327-0586

年份: 2015

卷: 2015-November

页码: 1010-1013

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 13

ESI高被引论文在榜: 0 展开所有

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