Advances in the Dempster-Shafer theory of evidence
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Covering with Reducts - A Fast Algorithm for Rule Generation
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Situation Identification by Unmanned Aerial Vehicle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
A Logic Programming Framework for Rough Sets
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A view on rough set concept approximations
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
An Approach to Pattern Recognition Based on Hierarchical Granular Computing
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
Minimal Description and Maximal Description in Covering-based Rough Sets
Fundamenta Informaticae
Perspectives on Uncertainty and Risk in Rough Sets and Interactive Rough-Granular Computing
Fundamenta Informaticae - Dedicated to the Memory of Professor Manfred Kudlek
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The concept of approximation is one of the most fundamental in rough set theory. In this work we examine this basic notion as well as its extensions and modifications. The goal is to construct a parameterized approximation mechanism making it possible to develop multi-stage multi-level concept hierarchies that are capable of maintaining acceptable level of imprecision from input to output.