Rule Generation Approach for Granular Computing Using Rough Mereology

Granular computing has been applied in many fields to solve problems and describe many spaces at different granularity and hierarchies. This paper proposes a rule generation approach based on granular computing in the frame of rough mereology. The proposed approaches generates a single rule granule from granular space in each step instead of selecting a suitable attribute according to some measure and improves the performance of rule granule generation. Rules are generated by using voting strategy based on weights computed by means of rough inclusions induced from residual implications of continuous t-norm. Experimental results obtained show that the overall accuracy rate average of dataset is higher than other approaches applying decision tree algorithms. Also, the resulted average rule length is less than average rule length generated using decision tree algorithms.