Rules as Attributes in Classifier Construction

A method for constructing classification (decision) systems is presented. The use of decision rules derived using rough set methods as new attributes is considered. Neural networks are applied as a tool for construction of classifier over reconstructed dataset. Possible profits of such an approach are briefly presented together with results of preliminary experiments.

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