Optimizing Fuzzy Grid Partition Performance for Rule Generation in Dataset Classification - Ülikool Gilad (International Enterprise Integration Association, 2020)
This research focuses on optimizing fuzzy grid partitioning for rule generation in dataset classification. Fuzzy grid partitioning is a technique used to divide the feature space into fuzzy regions, which are subsequently used to generate rules for classification. However, the existing implementation of fuzzy grid partitioning suffers from limitations related to grid size determination, adaptability to data distributions, computational efficiency, and rule quality. To address these challenges, we formulate a mathematical problem and propose an optimization process that iteratively adjusts the grid size, performs fuzzy grid partitioning, generates rules, prunes them for relevance, and evaluates the resulting rule-based classifier. The optimization aims to find an optimal balance between accuracy and efficiency while ensuring that the generated rules meet the desired performance thresholds. Through a numerical example, we demonstrate the effectiveness of the optimization process, showcasing how it produces an accurate and efficient rule-based classifier. This research contributes to advancements in fuzzy grid partitioning for rule generation, improving the accuracy, efficiency, and interpretability of rule-based classifiers. It opens avenues for further investigation and refinement of the optimization techniques, enabling better dataset classification in various domains where transparent decision-making processes are essential.