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» Rough Set Approximation Based on Dynamic Granulation
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ISMIS
2003
Springer
13 years 9 months ago
Granular Computing Based on Rough Sets, Quotient Space Theory, and Belief Functions
Abstract. A model of granular computing (GrC) is proposed by reformulating, re-interpreting, and combining results from rough sets, quotient space theory, and belief functions. Two...
Y. Y. Yao, Churn-Jung Liau, Ning Zhong
GRC
2007
IEEE
13 years 10 months ago
MGRS in Incomplete Information Systems
The original rough set model is concerned primarily with the approximation of sets described by single binary relation on the universe. In the view of granular computing, classica...
Yuhua Qian, Jiye Liang, Chuangyin Dang
IJAR
2008
82views more  IJAR 2008»
13 years 4 months ago
Probabilistic rough set approximations
This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize pa...
Yiyu Yao
RSCTC
2004
Springer
144views Fuzzy Logic» more  RSCTC 2004»
13 years 9 months ago
Approximation Spaces and Information Granulation
Abstract. In this paper, we discuss approximation spaces in a granular computing framework. Such approximation spaces generalise the approaches to concept approximation existing in...
Andrzej Skowron, Roman W. Swiniarski, Piotr Synak
RSEISP
2007
Springer
13 years 10 months ago
Interpreting Low and High Order Rules: A Granular Computing Approach
The main objective of this paper is to provide a granular computing based interpretation of rules representing two levels of knowledge. This is done by adopting and adapting the de...
Yiyu Yao, Bing Zhou, Yaohua Chen