An Improved Learning Algorithm of Decision Tree Based on Entropy Uncertainty Deviation

摘要：

　　Aimed at the problem of deviation of uncertainty estimates in the test model of attributes selecting with the information gain,an improved learning algorithm of decision tree based on the uncertainty deviation of entropy measure was developed.In the algorithm,the method of regulating oppositely deviation of the information entropy peak through a sine function was used,when test of attributes choice with information gain the adverse effect of deviation of information entropy peak was restrained.Compared with the ID3,the improvement of classification performance was acquired while its better stability of performance for its decision tree.The research results show that the rationality of attribute selection test was effectively improved through the method based on the entropy uncertainty deviation.

关键词： Learning algorithm decision tree information entropy uncertainty deviation

作者: Huaining Sun Xuegang Hu

作者单位: Department of Computer and Information Engineering Huainan Normal University Huainan, China School of Computer and Information Hefei University of Technology Hefei, China

会议类型: 国际会议

会议名称: 2012 IEEE 14th International Conference on Communication Technology(2012年第十四届通信技术国际会议(ICCT 2012))

会议地点: 成都

会议语种:英文

页码: 886-890

在线出版日期: 2012-11-09（万方平台首次上网日期，不代表论文的发表时间）