Variable precision rough set model
Journal of Computer and System Sciences
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Toward Intelligent Systems: Calculi of Information Granules
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Rough Set Approach to the Survival Analysis
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Calculi of Approximation Spaces
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Nearness of Objects: Extension of Approximation Space Model
Fundamenta Informaticae - Special Issue on Concurrency Specification and Programming (CS&P)
Handbook of Granular Computing
Handbook of Granular Computing
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
Relationship between generalized rough sets based on binary relation and covering
Information Sciences: an International Journal
Dominance-Based Rough Set Approach and Bipolar Abstract Rough Approximation Spaces
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Probabilistic modelling, inference and learning using logical theories
Annals of Mathematics and Artificial Intelligence
Information Entropy and Granulation Co---Entropy of Partitions and Coverings: A Summary
Transactions on Rough Sets X
A wistech paradigm for intelligent systems
Transactions on rough sets VI
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Transactions on Rough Sets V
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa
Software defect prediction based on source code metrics time series
Transactions on rough sets XIII
Information systems in modeling interactive computations on granules
Theoretical Computer Science
Modeling rough granular computing based on approximation spaces
Information Sciences: an International Journal
Information Sciences: an International Journal
Function Approximation and Quality Measures in Rough-Granular Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Interactive information systems: Toward perception based computing
Theoretical Computer Science
Knowledge Reduction in Random Incomplete Decision Tables via Evidence Theory
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Attribute Reduction Using Extension of Covering Approximation Space
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Rough Set Based Reasoning About Changes
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Automated Generation of Logical Constraints on Approximation Spaces Using Quantifier Elimination
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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We discuss some generalizations of the approximation space definition introduced in 1994 [24, 25]. These generalizations are motivated by real-life applications. Rough set based strategies for extension of such generalized approximation spaces from samples of objects onto their extensions are discussed. This enables us to present the uniform foundations for inducing approximations of different kinds of granules such as concepts, classifications, or functions. In particular, we emphasize the fundamental role of approximation spaces for inducing diverse kinds of classifiers used in machine learning or data mining.