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.
出处
《计算机工程与应用》
CSCD
北大核心
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)
关键词
可变精度粗糙集
决策树
置信度
数据挖掘
variable precisiou rough set
decision tree
confidence
data mining
