Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Attribute Discovery and Rough Sets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
AMAST '95 Proceedings of the 4th International Conference on Algebraic Methodology and Software Technology
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
On Generalizing Pawlak Approximation Operators
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Approximation Spaces in Extensions of Rough Set Theory
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Fuzzy Similarity Relation as a Basis for Rough Approximations
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Fuzzy Extension of Rough Sets Theory
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Fundamenta Informaticae
Tolerance Approximation Spaces
Fundamenta Informaticae
Decision Rule Based Data Models Using NetTRS System Overview
Transactions on Rough Sets IX
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Rough set methodology is based on concept (set) approximations constructed from available background knowledge represented in information systems [14]. In many applications only partial knowledge about approximated concepts is given. Hence quite often first a parametrized family of concept approximations is built and next, by parameters tuning the best, in a sense, approximation is chosen (see e.g. the variable precision rough set model [40]). In this paper we follow this approach in generalized approximation spaces. We discuss rough set model based on approximation spaces with uncertainty functions and rough inclusions. Elements of approximation space are parametrized, moreover for the proper application of such model to a particular data set it is necessary to make optimization of the parameters. We discuss not only basic properties of the mentioned model, but strategies of parameters optimization as well. We also present different notions of rough relations. Optimization of different parameters can be based on the degree of inclusion of relations defined by condition and decision attributes. Some illustration of presented methods on real life medical data set is also included.