决策树算法及其核心技术  

Decision Tree and Its Key Techniques

作　　者：杨学兵[1] 张俊[1] 

机构地区：[1]安徽工业大学计算机学院,安徽马鞍山243002

出　　处：《计算机技术与发展》2007年第1期43-45,共3页Computer Technology and Development

基　　金：安徽省教育厅自然科学基金重点资助项目(2004KJ053ZD)

摘　　要：决策树是归纳学习和数据挖掘的重要方法,通常用来形成分类器和预测模型。概述了决策树分类算法,指出了决策树算法的核心技术:测试属性的选择和树枝修剪技术。通过对当前数据挖掘中具有代表性的优秀分类算法进行分析和比较,总结出了各种算法的特性,为使用者选择算法或研究者改进算法提供了依据。最后,通过一个实例说明决策树分类在实际生产中的应用。Decision tree is an important method in induction learning as well as in data mining, which can be used to form classification and predictive model. Introduces decision tree and points out its key techniques： the choice of testing feature and tree pruning. It summarizes the main features of every algorithm by analyzing and comparing a variety of typical classifiers to provide a basis for selecting or improving the algorithms in data mining. Finally, through an instance, this paper shows the application of decision tree in production,

关 键 词：决策树 测试属性 树枝修剪 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]