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Rough Sets and Knowledge Technology, Third International Conference, RSKT 2008, Chengdu, China, May 17-19, 2008. Proceedings; 01/2008
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Rough Sets and Current Trends in Computing, 6th International Conference, RSCTC 2008, Akron, OH, USA, October 23-25, 2008, Proceedings; 01/2008
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Wei-Zhi Wu
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ABSTRACT: Knowledge reduction is an important issue in knowledge representation and knowledge discovery. This paper deals with knowledge reduction in consistent incomplete decision systems based on Dempster-Shafer theory of evidence. We show that, in a consistent incomplete decision system, concepts of both of belief reduct and plausibility reduct are equivalent to the concept of relative reduct.
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on; 09/2007
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ABSTRACT: In this paper, rough approximations in intuitionistic fuzzy set theory are discussed. The concepts of intuitionistic rough fuzzy sets and intuitionistic fuzzy rough sets are introduced. Their basic properties and operations are examined.
Machine Learning and Cybernetics, 2007 International Conference on; 09/2007
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ABSTRACT: In this paper three types of information granular structures, called similarity classes, maximal consistent blocks, and labeled blocks, in incomplete information systems are introduced. Their properties are examined. Based on the three structures of granules, three types of rough set approximation models are derived for mining of certain and possible rules in incomplete decision tables. The relationships among the three rough set models are established.
Machine Learning and Cybernetics, 2007 International Conference on; 09/2007
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ABSTRACT: The key to granular computing (GrC) is to make use of granules in problem solving. Classification is one of important problems in machine learning and data mining. With view of granular computing, this paper presents a classification approach to granules based on the variable precision rough set (VPRS) model. An algorithm is proposed and a tree structure of granules is given.
Cognitive Informatics, 6th IEEE International Conference on; 09/2007
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ABSTRACT: A family of overlapping granules can be formed by granulating a finite universe under a binary relation in a set-theoretic
setting. In this paper, we granulate a universe by a binary relation and obtain a granular universe. And then we define two
kinds of operators between these two universes, study properties of them. By combining these two kinds of operators, we get
two pairs of approximation operators. It is proved that one kind of combination operators is just the approximation operators
under a generalized approximation space defined according to Pawlak’s rough set theory.
06/2007: pages 93-100;
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Rough Sets and Knowledge Technology, Second International Conference, RSKT 2007, Toronto, Canada, May 14-16, 2007, Proceedings; 01/2007
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International Journal of Geographical Information Science. 01/2007; 21:1033-1058.
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ABSTRACT: A rough fuzzy set is a pair of fuzzy sets resulting from the approximation of a fuzzy set in a crisp approximation space.
A rough fuzzy set algebra is a fuzzy set algebra with added dual pair of rough fuzzy approximation operators. In this paper,
structures of rough fuzzy set algebras are studied. It is proved that if a system (F(U), Ç, È, ~ , L, H)({\cal F}(U), \cap, \cup, \sim, L, H) is a (a serial, a reflexive, a symmetric, a transitive, an Euclidean, a Pawlak, respectively) rough fuzzy set algebra then
the derived system (F(U), Ç, È, ~ , LL, HH)({\cal F}(U), \cap, \cup, \sim, LL, HH) is a (a serial, a reflexive, a symmetric, a transitive, an Euclidean, a Pawlak, respectively) rough fuzzy set algebra. Properties
of rough fuzzy approximation operators in different types of rough fuzzy set algebras are also examined.
KeywordsApproximation operators-Fuzzy sets-Rough fuzzy set algebras-Rough fuzzy sets-Rough sets
09/2006: pages 256-265;
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Granular Computing, 2006 IEEE International Conference on; 06/2006
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ABSTRACT: This paper deals with knowledge acquisition in incomplete information systems using rough set theory. The concept of similarity classes in incomplete information systems is first proposed. Two kinds of partitions, lower and upper approximations, are then formed for the mining of certain and association rules in incomplete decision tables. One type of “optimal certain” and two types of “optimal association” decision rules are generated. Two new quantitative measures, “random certainty factor” and “random coverage factor”, associated with each decision rule are further proposed to explain relationships between the condition and decision parts of a rule in incomplete decision tables. The reduction of descriptors and induction of optimal rules in such tables are also examined.
European Journal of Operational Research 02/2006; · 1.82 Impact Factor
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Rough Sets and Knowledge Technology, First International Conference, RSKT 2006, Chongqing, China, July 24-26, 2006, Proceedings; 01/2006
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01/2006;
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Fuzzy Systems and Knowledge Discovery, Third International Conference, FSKD 2006, Xi'an, China, September 24-28, 2006, Proceedings; 01/2006
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Rough Sets and Knowledge Technology, First International Conference, RSKT 2006, Chongqing, China, July 24-26, 2006, Proceedings; 01/2006
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ABSTRACT: We discuss characterizations of three important types of attribute sets in generalized approximation representation spaces,
in which binary relations on the universe are reflexive. Many information tables, such as consistent or inconsistent decision
tables, variable precision rough set models, consistent decision tables with ordered valued domains and with continuous valued
domains, and decision tables with fuzzy decisions, can be unified to generalized approximation representation spaces. A general
approach to knowledge reduction based on rough set theory is proposed.
09/2005: pages 84-93;
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Wei-Zhi Wu
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ABSTRACT: This paper deals with knowledge acquisition in incomplete information systems using variable rough set model. We introduce the concepts of β-lower and β-upper approximations. We also propose reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making within β precision. In our approach we make only one assumption about unknown values: the real value of a missing attribute is one from the attribute domain. We show how to find decision rules directly from such an incomplete decision table, which are as little non-deterministic as possible and have minimal number of conditions.
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on; 09/2005
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ABSTRACT: In this paper similarity relations and labeled block sets in incomplete information systems are introduced. Based on the two structures of granules, two rough set models are derived for mining of certain and possible rules in incomplete decision tables. The relationship between the two rough set models is examined.
Granular Computing, 2005 IEEE International Conference on; 08/2005
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ABSTRACT: This paper extends Pawlak's rough set onto the basis of a fuzzy partition of the universe of discourse. Some basic properties of partition-based fuzzy approximation operators are examined. To measure uncertainty in generalized fuzzy rough sets, a new notion of entropy of a fuzzy set is introduced. The notion is demonstrated to be adequate for measuring the fuzziness of a fuzzy event. The entropy of a fuzzy partition and conditional entropy are also proposed. These kinds of entropy satisfy some basic properties similar to those of Shannon's entropy. It is proved that the measure of fuzziness of a partition-based fuzzy rough set, FR( A ), is equal to zero if and only if the set A is crisp and definable.
International Journal of General Systems 01/2005; 34(1):77-90. · 0.67 Impact Factor
