计算机工程 ›› 2012, Vol. 38 ›› Issue (14): 41-43.doi: 10.3969/j.issn.1000-3428.2012.14.012

• 软件技术与数据库 • 上一篇    下一篇

基于朴素贝叶斯与ID3算法的决策树分类

黄宇达 1,2,王迤冉 3   

  1. (1. 西南科技大学计算机科学与技术学院,四川 绵阳 621010;2. 周口职业技术学院信息工程系,河南 周口 466000;3. 周口师范学院计算机科学与技术学院,河南 周口 466000)
  • 收稿日期:2011-09-24 出版日期:2012-07-20 发布日期:2012-07-20
  • 作者简介:黄宇达(1975-),男,讲师、硕士研究生,主研方向:数据挖掘,信息安全,分布式系统;王迤冉,副教授、硕士
  • 基金项目:

    河南省教育厅自然科学研究计划基金资助项目(2008B520 047);河南省科技厅基础与前沿技术研究计划基金资助项目(112300 410307)

Decision Tree Classification Based on Naive Bayesian and ID3 Algorithm

HUANG Yu-da 1,2, WANG Yi-ran 3   

  1. (1. College of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China; 2. Department of Information Engineering, Zhoukou Vocational and Technical College, Zhoukou 466000, China; 3. College of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466000, China)
  • Received:2011-09-24 Online:2012-07-20 Published:2012-07-20

摘要: v在朴素贝叶斯算法和ID3算法的基础上,提出一种改进的决策树分类算法。引入客观属性重要度参数,给出弱化的朴素贝叶斯条件独立性假设,并采用加权独立信息熵作为分类属性的选取标准。理论分析和实验结果表明,改进算法能在一定程度上克服ID3算法的多值偏向问题,并且具有较高的执行效率和分类准确度。

关键词: 朴素贝叶斯算法, ID3算法, 信息增益, 客观属性重要度, 条件独立性假设, 加权独立信息熵

Abstract: This paper proposes an improved decision tree classification algorithm based on naive Bayes algorithm and ID3 algorithm. It introduces objective attribute importance parameter, gives a kind of conditional independence assumption that is weaker than naive Bayesian algorithm, and uses the weighted independent information entropy as splitting attribute’s selection criteria. Theoretical analysis and experimental results show that the improved algorithm, to a certain extent well overcomes ID3 algorithm’s shortcoming of multi-value tendency, and improves algorithm’s implementation efficiency and classification accuracy.

Key words: naive Bayesian algorithm, ID3 algorithm, information gain, objective attribute importance, conditional independence assumption, weighted independent information entropy

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