Sensitivity analysis of rough classification
International Journal of Man-Machine Studies
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Handling Various Types of Uncertainty in the Rough Set Approach
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Numerical and applicational aspects of fuzzy relational equations
Fuzzy Sets and Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Rough classification in incomplete information systems
Mathematical and Computer Modelling: An International Journal
Transactions on Rough Sets IX
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
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Rough set theory refers to classification of objects described by well-defined values of qualitative and quantitative attributes. The values of attributes defined for each pair [object, attribute], called descriptors, are assumed to be unique and precise. In practice, however, these attribute values may be neither unique nor precise, i.e. they can be uncertain. We are distinguishing four types of uncertainty affecting values of attributes: uncertain discretization of quantitative attributes, imprecision of values of numerical attributes, unknown (missing) values of attributes, multiple values possible for one pair [object, attribute]. We propose a special way of modelling the first three types of uncertainty using fuzzy sets, which boils them down to the fourth type, called shortly, multiple descriptors. Thus, the generalization of the rough set approach consists in handling the case of multiple descriptors for both condition and decision attributes. The generalization preserves all characteristic features of the rough set approach while enabling reasoning about uncertain data. This capacity is illustrated by a simple example.