基于变精度粗糙集的决策树改进方法  

Improved method of decision tree based on variable precision rough set theory

作　　者：洪雪飞[1] 徐维祥[1] 

机构地区：[1]北京交通大学交通运输学院,北京100044

出　　处：《计算机工程与应用》2009年第13期163-165,共3页Computer Engineering and Applications

基　　金：北京市教育委员会科技发展计划重点项目(No.KZ2007100280414)

摘　　要：基于变精度粗糙集理论提出了具有置信度规则决策树的新的构造方法,该方法采用β-边界域的大小作为选择分类属性的标准,并对叶节点的置信度进行了重新的定义。经实验证明,该方法能有效提高分类效率且更加容易理解。In this paper,a new method of constructing decision tree with rules that have definite confidence is proposed based on variable precision rough sets theory.This method chooses the boundary region of rough sets as the criterion of selecting partitional attributes and redefines the conception of confidence of leaf nodes.The experiment shows that,decision tree built in this way is more effective and comprehensible.

关 键 词：可变精度粗糙集 决策树 置信度 数据挖掘 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]