Induction of fuzzy decision rules based upon rough sets theory

This work describes a system which tries to join the advantages of rough sets methods and fuzzy sets methods to improve classification processes. The fuzzy set theory supports approximate reasoning and the rough sets theory is responsible for data analysis and processing of automatic fuzzy rules generation. This system was designed as a typical knowledge based system, which contains four main parts: rule extractor, knowledge base, inference engine and user interface.