Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification: Enhancing Accuracy and Interpretability for Complex Data Analysis

Abstract

This research proposes a novel approach that combines hybrid grid partitioning, fuzzy rule generation, and rough set theory to enhance the accuracy and interpretability of dataset classification in complex data analysis. The study addresses the limitations of traditional classification methods by leveraging grid partitioning to simplify the dataset representation and focus on relevant regions of the attribute space. Fuzzy rule generation captures uncertainties and enables a more nuanced classification by considering membership degrees. Additionally, rough set theory is employed to identify relevant attributes, reducing the complexity of the model and enhancing interpretability. The proposed approach is particularly suitable for complex datasets characterized by high dimensionality and uncertainties. Experimental evaluations demonstrate its effectiveness in improving accuracy and providing meaningful insights for decision-making. The research contributes to advancing the field of dataset classification by offering a comprehensive framework that combines grid partitioning, fuzzy rule generation, and rough set theory to tackle complex data analysis challenges.