![]() |
个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术. 计算机软件与理论
Knowledge Dependency and Rule Induction on Tolerance Rough Sets
点击次数:
论文类型:期刊论文
发表时间:2013-01-01
发表刊物:JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
收录刊物:SCIE、EI、Scopus
卷号:20
期号:3-4
页面范围:401-421
ISSN号:1542-3980
关键字:Rough set theory; tolerance relation; tolerance information table; knowledge dependency; rule induction
摘要:Classical rough set theory(RST) is based on equivalence relations. Tolerance relations are more generic than equivalence relations. We extend some concepts in classical RST to tolerance relations by proposing that the knowledge representation in rough set models based on tolerance relations, such as weak, strong and central dependency, as well as the relationships among them. A general complete theorem about knowledge representation is given. We give formal proofs of the theorem and verify its correctness with some examples. A case study is presented to show how to extract certain rules from an incomplete information table. It is more elaborate than the restriction of equivalence relations for the classical rough set theory. The proposed approach is indeed effective, and therefore of practical value to many real-world problems.