Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
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
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Maximal consistent block technique for rule acquisition in incomplete information systems
Information Sciences: an International Journal
Dominance relation and rules in an incomplete ordered information system
International Journal of Intelligent Systems
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Dominance-based rough set approach and knowledge reductions in incomplete ordered information system
Information Sciences: an International Journal
A Novel Extension of Rough Set Model in Incomplete Information System
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
On the compact computational domain of fuzzy-rough sets
Pattern Recognition Letters
Incomplete data and generalization of indiscernibility relation, definability, and approximations
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
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In this paper, we present an explorative research focusing on dominance-based rough set approach to the incomplete information systems. In most of the rough set literatures, an incomplete information system indicates an information system with unknown values. By assuming that the unknown value can be compared with any other values in the domain of the corresponding attributes, the concept of the valued dominance relation is proposed to show the probability that an object is dominating another one. The fuzzy rough approximations in terms of the valued dominance relation are then constructed. It is shown that by the valued dominance-based fuzzy rough set, we can obtain greater lower approximations and smaller upper approximations than the old dominance-based rough set in the incomplete information systems. Further on the problem of inducing "at least" and "at most" decision rules from incomplete decision system is also addressed. Some numerical examples are employed to substantiate the conceptual arguments.