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计算机工程 ›› 2009, Vol. 35 ›› Issue (8): 50-52. doi: 10.3969/j.issn.1000-3428.2009.08.017

• 软件技术与数据库 • 上一篇    下一篇

多关系决策树学习算法的研究与改进

谢志强1,于 旭1,杨 静2,刘若铎1   

  1. (1. 哈尔滨理工大学计算机科学与技术学院,哈尔滨 150080;2. 哈尔滨工程大学计算机科学与技术学院,哈尔滨 150001)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-04-20 发布日期:2009-04-20

Research and Improvement of Multi-relational Decision Tree Learning Algorithm

XIE Zhi-qiang1, YU Xu1, YANG Jing2, LIU Ruo-duo1   

  1. (1. College of Computer Science and Technique, Harbin University of Science and Technology, Harbin 150080;2. College of Computer Science and Technique, Harbin Engineering University, Harbin 150001)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-04-20 Published:2009-04-20

摘要: 通过对多关系决策树学习算法MRDTL-2进行研究与分析,针对其运行效率较低和不能有效处理丢失属性值的问题,提出一种改进的多关系数据挖掘(IMRDTL)算法。在IMRDTL算法中,利用元组ID传播技术来进一步提高MRDTL-2算法的运行效率,同时使用广义朴素贝叶斯分类器来填补丢失的属性值,以进一步提高算法的准确率。

关键词: 多关系数据挖掘, 决策树, 元组ID传播, 广义朴素贝叶斯

Abstract: Through researches and analyses on Multi-Relational Decision Tree Learning(MRDTL) algorithm, MRDTL-2, for the problems of low running efficiency and poor effect in dealing with missing attribute values of it, this paper introduces an Improved Multi-Relational Data Mining (IMRDTL) algorithm. IMRDTL algorithm makes use of the tuple ID propagation for speeding up the efficiency of MRDTL-2 algorithm, meanwhile, generalized naive Bayes classifier is used to fill in missing attribute values, so the algorithm can provide a better accuracy.

Key words: Multi-Relational Data Mining(MRDM), decision tree, tuple ID propagation, generalized naive Bayes

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