<![CDATA[Accurate dataset classification is a fundamental task in various domains such as machine learning, pattern recognition, and data mining. This research proposes a novel hybrid approach that combines grid partitioning, rough set-based feature reduction, and fuzzy rule generation to enhance classification accuracy and interpretability. The approach begins with the partitioning of the dataset into a grid of cells, enabling localized analysis and capturing intricate patterns. Next, rough set-based feature reduction is applied to identify essential features and reduce dimensionality. This process helps overcome the curse of dimensionality commonly associated with complex datasets. Subsequently, fuzzy rule generation is employed, leveraging linguistic variables and membership functions to represent imprecise and uncertain information. This enhances interpretability by providing transparent decision-making rules. To evaluate the effectiveness of the proposed approach, comparative analysis with traditional classification methods, including decision trees, support vector machines, and neural networks, is conducted. The results demonstrate the superiority or at least comparability of the hybrid approach in terms of classification accuracy, computational complexity, and interpretability. However, it is essential to acknowledge the limitations of the research, such as the sensitivity to grid size and the interpretability-performance trade-off. Future research can focus on refining the approach by exploring optimal grid size selection methods and mitigating the interpretability-performance trade-off.The findings of this research contribute to the advancement of accurate dataset classification techniques. The proposed hybrid approach offers improved classification accuracy, handles complex datasets effectively, and enhances interpretability through fuzzy rules. The practical implications of the research span domains such as bioinformatics, IoT, and financial analysis. Overall, this research provides a foundation for further exploration, refinement, and real-world applications of the hybrid approach in accurate dataset classification scenarios.]]>
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