On Databases with Incomplete Information
Journal of the ACM (JACM)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Reducing Information Systems with Uncertain Attributes
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Rough Set Analysis of Preference-Ordered Data
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Generalized Decision Logic in Interval-Set-Valued Information Tables
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Lower and Upper Approximations of Rules in Non-deterministic Information Systems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Rough Set Approach to Rule Induction from Imprecise Decision Tables
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
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In this paper, we propose a dominance-based fuzzy rough set approach for the decision analysis of a preference-ordered possibilistic information systems, which is comprised of a finite set of objects described by a finite set of criteria. The domains of the criteria may have ordinal properties that express preference scales. In the proposed approach, we first compute the degree of dominance between any two objects based on their possibilistic evaluations with respect to each criterion. This results in a fuzzy dominance relation on the universe. Then, we define the degree of adherence to the dominance principle by every pair of objects and the degree of consistency of each object. The consistency degrees of all objects are aggregated to derive the quality of the classification, which we use to define the reducts of an information system. In addition, the upward and downward unions of decision classes are fuzzy subsets of the universe. The lower and upper approximations of the decision classes based on the fuzzy dominance relation are thus fuzzy rough sets. By using the lower approximations of the decision classes, we can derive two types of decision rules that can be applied to new decision cases..