Comparison of the probabilistic approximate classification and the fuzzy set model
Fuzzy Sets and Systems
Rough approximation of shapes in pattern recognition
Computer Vision, Graphics, and Image Processing
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough mereological foundations for design, analysis, synthesis, and control in distributed systems
Information Sciences: an International Journal - From rough sets to soft computing
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Inclusion degree: a perspetive on measures for rough set data analysis
Information Sciences—Informatics and Computer Science: An International Journal
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
A novel approach to fuzzy rough sets based on a fuzzy covering
Information Sciences: an International Journal
Rough set theory for the interval-valued fuzzy information systems
Information Sciences: an International Journal
Random and fuzzy sets in coarse data analysis
Computational Statistics & Data Analysis
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy Rough Sets: The Forgotten Step
IEEE Transactions on Fuzzy Systems
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
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Given a widespread interest in rough sets as being applied to various tasks of data analysis, it is not surprising at all that we have witnessed a wave of further generalizations and algorithmic enhancements of this original concept. In this study, we investigate an idea of rough fuzzy random sets. This construct provides us with a certain generalization of rough sets by introducing the concept of inclusion degree. The underlying objective behind this development is to address the problems which involve co-existing factors of fuzziness and randomness thus giving rise to a notion of the fuzzy random approximation space based on inclusion degree. Some essential properties of rough approximation operators of such rough fuzzy random sets are discussed. Further theoretical foundations for the formation of rules constructed on a basis of available decision tables are offered as well.