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决策树的并行训练策略 被引量：1

Parallel Strategies for Training Decision Tree
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摘要 随着生物科学技术的发展,其数据量的增长也非常迅速,很难在一定合理的时间内对数据进行建模和分析,因此,对并行数据挖掘算法的研究已变成解决此问题的重要途径。决策树途径已被广泛用作一种重要的分类工具,本文研究了几种决策树的并行训练策略并对它们的性能进行了比较。 With the development of biology technology ,the amounts of data increase very fast. It is difficult to analyze and model the data within reasonable amount of time ,so studying parallel data mining algorithms has been becoming an important approach to solving the problem. Decision tree has been widely applied as an important classification tool. This paper surveys several parallel training decision tree strategies and compares their performance.
作者 刘欣阳 王国仁 乔百友 韩东红
机构地区 东北大学信息学院
出处 《计算机科学》 CSCD 北大核心 2004年第8期129-130,135,共3页 Computer Science
基金 国家自然科学基金项目资助(60273079)。
关键词 决策树 并行训练 数据挖掘 动态数据分片 人工神经元网络 统计模型 Decision tree Parallel training Data mining
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献11

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同被引文献15

  • 1Dwork C. A Firm Foundation for Private Data Dnalysis[J]. Communications of the ACM,2011,54(1) :86-95.
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  • 8Barak B, Chaudhuri K, Dwork C, et al. Privacy, accuracy, and consistency too: a holistie solution to contingency table release [C]//PODS'07. Beijing,China: Association for Computing Ma- chinery, Inc, 2007 : 273-282.
  • 9Dwork C. Differential privacy: A survey of Results[C]// TAMC' 08. Xi'an, China :Pringer-Verlag, 2008:1-19.
  • 10Dwork C, MeSherry F, Nissim K, et al. Calibrating noise to sen- sitivity in private data analysis[C]//TCC' 06. New York, NY, USA: Springer, 2006 : 265-284.

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计算机科学
2004年 第8期

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