摘要
不确定性度量是粗糙集理论研究的重要内容之一。分析了目前粗糙集不确定性度量主要方法的不足,给出了基于边界域的粗糙集粗糙边界熵的定义。证明了这种粗糙边界熵随着知识粒度的减小而单调减小,而且当负域的知识颗粒被细分时,粗糙边界熵不变。给出了粗糙边界熵的两条性质。
Uncertainty measure is one of important contents of rough set theory study. In this paper, by analyzing the problem of methods for measuring the uncertainty of rough set, the rough boundary entropy is defined, based on boundary region. It is proved that the rough boundary entropy decreases monotonously as the knowledge granularity becomes finer. When the knowledge granularity of negative region is subdivided, the rough boundary entropy doesn＇ t change. Two properties of rough boundary entropy are given.
出处
《计算机工程与应用》
CSCD
2012年第28期147-149,177,共4页
Computer Engineering and Applications
基金
安徽省高等学校省级自然科学研究项目(No.KJ2011B178)
宿州学院智能信息处理实验室开放课题(No.2010YKF10)
宿州学院一般科研项目(No.2011yyb04)
高等学校省级优秀青年人才基金项目(No.2011SQRL154)
关键词
粗糙集
信息系统
不确定性度量
粗糙边界熵
粗糙度
边界域
rough set
information system
uncertainty measure
rough boundary entropy
roughness
boundary region
