Rough sets: probabilistic versus deterministic approach
Machine learning and uncertain reasoning
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
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
The Paradox of the Heap of Grains in Respect to Roughness, Fuzziness and Negligibility
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Constraint Based Incremental Learning of Classification Rules
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Rough sets and information granulation
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2003)
A Note on 3-valued Rough Logic Accepting Decision Rules
Fundamenta Informaticae
Information systems in modeling interactive computations on granules
Theoretical Computer Science
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Interactive information systems: Toward perception based computing
Theoretical Computer Science
Approximation of sets based on partial covering
Transactions on Rough Sets XVI
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 approximation space definition has evolved in rough set theory over the last 15 years. The aim was to build a unified framework for concept approximations. We present an overview of this evolution together with some operations on approximation spaces that are used in searching for relevant approximation spaces. Among such operations are inductive extensions and granulations of approximation spaces. We emphasize important consequences of the paper for research on approximation of vague concepts and reasoning about them in the framework of adaptive learning. This requires developing new approach to vague concepts going beyond the traditional rough or fuzzy approaches.