Hybrid Grid Partition and Rough Set Method for Generation of Fuzzy Rules in Dataset Classification

Abstract

The Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification is a novel approach aimed at addressing the challenges of classifying datasets with uncertainty and imprecision. This methodology combines the concepts of grid partitioning, rough set theory, and fuzzy rule generation to enhance classification accuracy and interpretability. The hybrid grid partitioning technique divides the attribute space into a grid structure, capturing the underlying structure and relationships in the dataset. Rough set theory is then utilized to analyze the dataset and identify relevant attributes, reducing dimensionality and improving classification efficiency. Fuzzy rule generation employs fuzzy logic to capture imprecise and uncertain knowledge present in the dataset, generating flexible and robust fuzzy rules. Rule evaluation and selection processes are employed to identify high-quality rules for accurate and interpretable classification models. The proposed methodology offers a comprehensive framework for handling complex datasets, demonstrating improved classification performance in various domains. Experimental evaluations and comparisons with other classification approaches validate the effectiveness and practicality of the Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification. This research contributes to advancing the field of dataset classification, particularly in scenarios where uncertainty and imprecision are prevalent. The proposed approach offers a comprehensive framework for handling complex datasets and improving classification performance in various domains.