ATTRIBUTE REDUCTION ALGORITHM BASED ON COGNITIVE MODEL OF GRANULAR COMPUTING IN INCONSISTENT DECISION INFORMATION SYSTEMS

This article aims to explore a new method of attribute reduction in inconsistent decision information systems. By analyzing the connection of attribute reduction theory and cognitive science, an attribute reduction algorithm based on cognitive model of granular computing is proposed in this paper. Algorithm analysis and numerical experiment show the validity of the proposed attribute reductions algorithm. The method can be applied to both consistent and inconsistent information systems. The proposed model also provides a new model and thinking to study the connection of human's cognition and notion. It is useful to the development of cognitive model.