Extensions and intentions in the rough set theory
Information Sciences: an International Journal
Rough set approach to incomplete information systems
Information Sciences: an International Journal
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
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
On knowledge reduction in inconsistent decision information systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information Sciences: an International Journal
On Three Types of Covering-Based Rough Sets
IEEE Transactions on Knowledge and Data Engineering
Measures for evaluating the decision performance of a decision table in rough set theory
Information Sciences: an International Journal
Consistency measure, inclusion degree and fuzzy measure in decision tables
Fuzzy Sets and Systems
MGRS: A multi-granulation rough set
Information Sciences: an International Journal
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Axiomatic approach of knowledge granulation in information system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Learning rule representations from data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Covering based rough set approximations
Information Sciences: an International Journal
NMGRS: Neighborhood-based multigranulation rough sets
International Journal of Approximate Reasoning
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In view of granular computing, the classical optimistic and pessimistic multigranulation rough set models are both primarily based on simple granules among multiple granular structures, namely multiple partitions of the universe in MGRS. This correspondence paper presents a new rough set model where set approximations are defined by using multiple coverings on the universe. In order to distinguish Qian's covering-based optimistic multigranulation rough set model, we call the new rough set model as covering-based pessimistic multigranulation rough set model. The key distinction between covering-based pessimistic multigranulation rough set model and Qian's covering-based optimistic multigranulation rough set model is set approximation descriptions. Then some properties are proposed for covering-based pessimistic multigranulation rough set model.