<![CDATA[This research focuses on optimizing the performance of fuzzy grid partitioning for rule generation in dataset classification. The objective is to develop an approach that improves classification accuracy while maintaining interpretability and considering practical constraints. The research introduces a novel optimization framework that balances accuracy and complexity through an objective function. Fuzzy sets and a grid structure are defined, and a rule base is generated based on the fuzzy grid and classification outcomes. The proposed approach demonstrates enhanced classification accuracy compared to traditional methods, capturing underlying patterns effectively. Additionally, the approach achieves improved interpretability by incorporating complexity constraints. The research addresses scalability and compares the approach with existing techniques. The findings contribute to the field of rule-based classifiers, providing insights into accurate and interpretable classification models with practical applicability in various domains. Future research directions include generalizability, parameter sensitivity, and comparison with state-of-the-art techniques.]]>
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