A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.
For example in REF , Leung et al. proposed a rough set approach to discover classification rules for the continuous valued information systems.
A rough set approach for the discovery of classification rules in interval-valued information systems
Int. J. Approx. Reason.
Int. J. Approx. Reason.
Mathematics Computer Science