Optimizing Fuzzy Rule Generation: A Grid Partitioning and Rough Set Method Approach for Enhanced Accuracy and Interpretability

This research focuses on optimizing fuzzy rule generation through the application of grid partitioning and rough set method, with the aim of enhancing both accuracy and interpretability. The proposed mathematical model addresses the challenge of generating accurate and interpretable fuzzy rule sets, particularly in the context of credit risk assessment. By utilizing grid partitioning, the input space is divided into regions, while the rough set method is employed to identify relevant features. The results show improved accuracy in classifying loan applicants into low-risk and high-risk categories, accompanied by enhanced interpretability through the generation of clear and understandable rules. The model's applicability extends to credit risk assessment and offers potential for further refinement and research. However, it is crucial to consider certain limitations, including the generalizability of results, sensitivity to grid partitioning, and the trade-off between accuracy and interpretability. In conclusion, the proposed model exhibits promise in generating accurate and interpretable fuzzy rule sets, thereby contributing to effective decision-making processes across diverse domains.