Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification

This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances the representation and separation of data regions, improving classification accuracy. The generated fuzzy rule base provides interpretable decision rules, enabling domain experts to gain insights into the classification process. The proposed approach strikes a balance between accuracy and interpretability, making it valuable for various domains. However, limitations such as generalizability and scalability should be considered. Comparative analysis with existing methods and real-world case studies would further validate the effectiveness of the approach. Overall, this research contributes to the advancement of dataset classification and provides a novel integrated approach for accurate and interpretable classification.