Extensions and intentions in the rough set theory
Information Sciences: an International Journal
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Rough approximation quality revisited
Artificial Intelligence
Rough set theory applied to (fuzzy) ideal theory
Fuzzy Sets and Systems
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Inclusion degree: a perspetive on measures for rough set data analysis
Information Sciences—Informatics and Computer Science: An International Journal
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Information Sciences—Informatics and Computer Science: An International Journal
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
An axiomatic characterization of a fuzzy generalization of rough sets
Information Sciences—Informatics and Computer Science: An International Journal
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
An improved accuracy measure for rough sets
Journal of Computer and System Sciences
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Learning fuzzy rules from fuzzy samples based on rough set technique
Information Sciences: an International Journal
Information Sciences: an International Journal
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Reasoning with rough description logics: An approximate concepts approach
Information Sciences: an International Journal
Reasoning within expressive fuzzy rough description logics
Fuzzy Sets and Systems
Attribute dependency functions considering data efficiency
International Journal of Approximate Reasoning
On generalized fuzzy belief functions in infinite spaces
IEEE Transactions on Fuzzy Systems
A new uncertainty measure of rough sets
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems
Fuzzy Sets and Systems
Fuzzy rough set based attribute reduction for information systems with fuzzy decisions
Knowledge-Based Systems
The reduction and fusion of fuzzy covering systems based on the evidence theory
International Journal of Approximate Reasoning
On Some Mathematical Structures of T-Fuzzy Rough Set Algebras in Infinite Universes of Discourse
Fundamenta Informaticae - Advances in Rough Set Theory
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Fuzzy rough set is a generalization of crisp rough set, which deals with both fuzziness and vagueness in data. The measures of fuzzy rough sets aim to dig its numeral characters in order to analyze data effectively. In this paper we first develop a method to compute the cardinality of fuzzy set on a probabilistic space, and then propose a real number valued function for each approximation operator of the general fuzzy rough sets on a probabilistic space to measure its approximate accuracy. The functions of lower and upper approximation operators are natural generalizations of the belief function and plausibility function in Dempster-Shafer theory of evidence, respectively. By using these functions, accuracy measure, roughness degree, dependency function, entropy and conditional entropy of general fuzzy rough set are proposed, and the relative reduction of fuzzy decision system is also developed by using the dependency function and characterized by the conditional entropy. At last, these measure functions for approximation operators are characterized by axiomatic approaches.