A rough-set-based approach for classification and rule induction

The inconsistency of information about objects may be the greatest obstacle to performing inductive learning from examples. Rough sets theory provides a new mathematical tool to deal with uncertainty and vagueness. Based on rough sets theory, this paper proposes a novel approach far the classification and rule induction of inconsistent information systems. It is achieved by integrating rough sets theory with a statistics-based inductive learning algorithm. The framework of a prototype rough-set-based classification system (RClass) is presented. Two examples are used to verify the prototype system. The results of the validation are discussed.