Abstract
Accurate dataset classification is a critical task in various domains, and combining different methodologies can enhance classification performance. This research presents a novel approach that integrates Hybrid Grid Partition and Rough Set methods for fuzzy rule generation, aiming to improve accuracy and interpretability in dataset classification. The proposed approach leverages Hybrid Grid Partition to discretize continuous attributes and Rough Set attribute reduction to identify essential attributes, enabling accurate classification while handling uncertainty and imprecision. The generated fuzzy rules provide interpretability, aiding decision-making processes and providing insights into classification factors. The approach's robustness and generalization capabilities are demonstrated through experiments on diverse datasets, indicating its potential applicability in real-world scenarios. However, limitations such as the absence of specific evaluation metrics and the need for further validation on larger datasets are acknowledged. Overall, this research contributes to accurate dataset classification by offering a novel integrated approach and highlighting areas for future investigation and refinement
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