Hybridizing grid partitioning, rough set theory, and feature selection for fuzzy rule generation in dataset classification

This research investigates the hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation in Dataset Classification. The objective is to improve classification accuracy and interpretability by integrating multiple techniques. Grid partitioning is employed to divide the dataset into regions, allowing localized analysis. Rough set theory is utilized for attribute reduction and feature selection, identifying informative features within each region. Fuzzy rule generation is applied to generate interpretable classification rules using linguistic terms and membership functions. The hybrid model is optimized using metaheuristic algorithms to maximize classification performance. The research demonstrates the potential of the hybrid approach through experiments on the Iris flower dataset. The findings reveal improved classification accuracy, enhanced interpretability, and effective handling of complex datasets. The research contributes to the field by integrating these techniques into a cohesive framework and highlights the importance of parameter settings, computational complexity, and real-world applications. Future work should address these limitations and validate the approach on diverse datasets. The hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation holds promise for advancing classification models in various domains