Rough Sets

By T. Beaubouef, F. Petry. Database Technologies: Concepts, Methodologies, Tools, and Applications, 2009

6,212 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b85e370fc27a01b705e50835e8c02e949cfa96aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

Citations in the corpus

Attributes Reduction Using Fuzzy Rough Sets (2008, background): “T HE concept of rough sets was originally proposed by Pawlak [1] as a mathematical approach to handling imprecision, vagueness and uncertainty in data analysis.”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “In the standard and original version of rough set theory [148], the two approximations are crisp sets, but in other variations the approximating sets may be fuzzy sets.”
An introduction to fuzzy soft topological spaces (2015, methodology): “To exceed these uncertainties, some kinds of theories were given like theory of fuzzy sets [21], intuitionistic fuzzy sets [4], rough sets [16],i.”
Introduction to fuzzy soft groups (2009, neutral): “To exceed these uncertainties, some kinds of theories were given like theory of fuzzy sets [1], intuitionistic fuzzy sets [2], rough sets [3], i.”
Dimensionality reduction based on rough set theory: A review (2009, background): “Rough set theory was initially developed [76,77] for a finite universe of discourse in which the knowledge base is a partition, which is obtained by any equivalence relation defined on the universe of discourse.”
Soft sets and soft rough sets (2011, methodology): “Rough set theory was initiated by Pawlak [21] for dealing with vagueness and granularity in information systems.”
Attribute reduction in decision-theoretic rough set models (2008, methodology): “In recent years, researchers, motivated by a desire to represent information qualitatively, have proposed many models to incorporate probabilistic approaches into rough set theory, which was introduced by Pawlak [22,24,26,27].”
Handbook of Granular Computing (2008, methodology): “Otherwise, heuristic strategies as proposed in [33] might be used to find a suboptimal solution.”
FUZZY SOFT SET THEORY AND ITS APPLICATIONS (2011, background): “[20] worked on theoretical study of soft sets in detail, and [19] presented an application of soft set in the decision making problem using the reduction of rough sets [29].”
Vague soft sets and their properties (2010, background): “There have been a great amount of research and applications in the literature concerning some special tools like probability theory, fuzzy set theory [1], rough set theory [2], vague set theory [3], intuitionistic fuzzy set theory [4,5] and intervalmathematics [6,7]; each of these theories has its a”
A Historical Account of Types of Fuzzy Sets and Their Relationships (2016, methodology): “A. Fuzzy Rough Sets From the concept of rough set given by Pawlak [113], Dubois and Prade [50] propose the following definition.”