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决策树的优化算法
引用本文:刘小虎,李 生.决策树的优化算法[J].软件学报,1998,9(10):797-800.
作者姓名:刘小虎  李 生
作者单位:哈尔滨工业大学计算机科学系,哈尔滨,150001;哈尔滨工业大学计算机科学系,哈尔滨,150001
基金项目:本文研究得到国家863高科技项目基金资助.
摘    要:决策树的优化是决策树学习算法中十分重要的分支.以ID3为基础,提出了改进的优化算法.每当选择一个新的属性时,算法不是仅仅考虑该属性带来的信息增益,而是考虑到选择该属性后继续选择的属性带来的信息增益,即同时考虑树的两层结点.提出的改进算法的时间复杂性与ID3相同,对于逻辑表达式的归纳,改进算法明显优于ID3.

关 键 词:机器学习  决策树  分类  信息增益  熵.
收稿时间:1997/5/26 0:00:00
修稿时间:1997/9/15 0:00:00

An Optimized Algorithm of Decision Tree
LIU Xiao-hu and LI Sheng.An Optimized Algorithm of Decision Tree[J].Journal of Software,1998,9(10):797-800.
Authors:LIU Xiao-hu and LI Sheng
Institution:Department of Computer Science\ Harbin Institute of Technology\ Harbin\ 150001
Abstract:Optimization of decision-tree is a significant branch in decision-tree learning algorithm. An optimized learning algorithm of ID3, a typical decision-tree learning algorithm is presented in this paper. When the algorithm selects a new attribute, not only the information gain of the current attribute, but also the information gain of succeeding attributes of this attribute is taken into consideration. In other words, the information gain of attributes in two levels of the decision tree is used. The computational complexity of the modified ID3 (MID3) is the same as that of the ID3. When the two algorithms are applied to learning logic expressions, the performance of MID3 is better than that of ID3.
Keywords:Machine learning  decision-tree  classification  information gain  entropy  
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