Enhancing Accuracy and Interpretability in Dataset Classification: Advancements in Hybrid Grid Partition and Rough Set Methods for Fuzzy Rule Generation

Accurate and interpretable classification of datasets plays a crucial role in various domains, including healthcare, finance, and image recognition. This research focuses on enhancing accuracy and interpretability in dataset classification through the integration of hybrid grid partition and rough set methods for fuzzy rule generation. The proposed mathematical model leverages the grid partition approach to handle the curse of dimensionality and reduce dataset complexity, while the rough set method identifies essential features and generates meaningful fuzzy rules. The assigned membership values to linguistic terms further enhance interpretability. The model's accuracy and interpretability were evaluated using a diabetes dataset, achieving an accuracy rate of 85% on the validation dataset and 83% on the testing dataset. Comparative analysis demonstrated competitive performance against existing methods. The iterative refinement process contributed to the model's optimization. However, limitations include dataset dependency, parameter sensitivity, and scalability. Future research directions include advanced rule pruning techniques, optimization of model parameters, handling imbalanced datasets, incorporating feature selection, robustness and scalability evaluation, comparative studies, and real-world application validation. The proposed model presents a promising approach to enhance accuracy and interpretability in dataset classification.