An Enhanced Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Complex Dataset Problem
DOI:
https://doi.org/10.35335/emod.v14i3.35Keywords:
Fuzzy rule generation, Complex datasets, Hybrid grid partitioning, Rough set theory, InterpretabilityAbstract
This research presents an enhanced hybrid grid partitioning and rough set approach for fuzzy rule generation in complex datasets. Complex datasets pose challenges due to their high dimensionality, nonlinearity, and uncertainty, which traditional analysis techniques struggle to address. Fuzzy rule-based systems offer a promising solution, but generating effective fuzzy rules becomes increasingly difficult as the dataset complexity increases. To overcome these challenges, we propose a methodology that integrates hybrid grid partitioning and rough set theory. The hybrid partitioning technique captures global and local patterns, while rough set theory handles uncertainty and incomplete information. The approach generates accurate and interpretable fuzzy rules by optimizing the rule set's size and balancing accuracy and interpretability. Experimental evaluations demonstrate the approach's effectiveness in terms of rule accuracy, interpretability, and computational efficiency. The generated fuzzy rules provide valuable insights into complex relationships, aiding decision-making processes in various domains. The research contributes to the advancement of fuzzy rule generation techniques for complex datasets and offers a practical solution for knowledge extraction in complex systems.
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