摘要
不确定性度量是粗集理论研究的重要内容之一.基于信息论,结合Paw lak拓扑思想,提出了一般二元关系(自反性)下基于边界域的知识粗糙熵新定义,修正了粗集粗糙熵的定义.相对于传统粗糙熵,新的知识粗糙熵概念能更准确地度量知识和集合的不确定性,并在此基础上证明了新的知识粗糙熵和修正后的粗集粗糙熵都随知识分辨能力的增强而单调下降.
Uncertainty measure is one of the important aspects of rough set theory study. Based on information theory and Pawlak topology idea, the new definition about rough entropy of knowledge based on boundary region of general binary relations is presented, and the definitions of rough entropy of rough set is rectified. Compared with traditional rough entropy, both knowledge and rough set uncertainty can be measured more accurately under the new definitions. Meanwhile, it is proved that both the new rough entropy of knowledge and rectified rough entropy of rough set will be monotonously reduced with knowledge identifying ability enhanced.
出处
《四川师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2008年第3期273-277,共5页
Journal of Sichuan Normal University（Natural Science）
基金
四川省教育厅自然科学重点基金(2003A079)资助项目
关键词
粗糙集理论
一般二元关系
边界域
粗糙熵
知识粗糙熵
粗集粗糙熵
Rough sets theory
General binary relation
Boundary region
Rough entropy
Knowledge-based rough entropy
Rough-set based rough entropy
