Granular Computing and Rough Sets to Generate Fuzzy Rules

In the recent years, rough set theory has been applied in diverse areas of research, however its application to classification problems is still a challenger. In this paper we present a new method to automatically generate fuzzy rules using an extension of rough sets. We use genetic algorithm to determine the granules of the knowledge to obtain the rough sets. The resulting classifier system based on the set of fuzzy rules was tested with the public databases: Iris, Wine, and Wdbc datasets, presenting accuracy rates of 100%, 100%, and 99%, respectively.

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