Fuzzy Rule Generation Approach to Granular Computing Using Rough Mereology

In this paper we propose a fuzzy rule generation approach based on granular computing using rough mereology (FRGAGCRM). The proposed system works in two phases. In the first phase, the pre-processing phase which use fuzzification methodology which map the numeric dataset into categorical dataset according to membership function described in this paper. In other hand, the second phase consists of the rough mereology phase and rule generation phase. The rules are generated by rough mereology and rough inclusion techniques. The experimental results using Irvine, ML Repository dataset show that FRGAGCRM is better than RGAGCRM, ID3, and RGAGC. The performance measure of FRGAGCRM is compared with the performance of RGAGCRM, ID3, and RGAGC in same dataset. The result obtained empirically demonstrates that the proposed FRGAGCRM performs superior.