Enhancing Rule Generation in Dataset Classification through Optimized Fuzzy Grid Partitioning: A Performance Optimization Framework
DOI:
https://doi.org/10.35335/emod.v14i2.28Keywords:
Rule generation, Dataset classification, Optimized fuzzy grid partitioning, Performance optimization framework, AccuracyAbstract
The research aims to enhance rule generation in dataset classification through an optimized fuzzy grid partitioning framework. The proposed framework combines fuzzy grid partitioning, which captures uncertainty and ambiguity in datasets, with an optimization algorithm to improve the accuracy and efficiency of rule generation. The main contributions of this research include enhanced accuracy in dataset classification, improved computational efficiency, and emphasis on interpretability and transparency of the generated rules. The framework achieves improved accuracy by optimizing the fuzzy grid partitioning process to capture underlying patterns in the data. The incorporation of an optimization algorithm reduces the computational complexity, enabling faster classification on large-scale datasets. The generated rules are designed to be understandable to domain experts, enhancing transparency and facilitating decision-making. The research acknowledges certain limitations, such as algorithm dependence and the need for real-world validation. Future research directions include exploring alternative optimization algorithms, conducting extensive evaluations on diverse datasets, and validating the framework's performance in real-world applications. Overall, the proposed framework offers a valuable contribution to the field of data mining and machine learning, providing an effective approach to enhance rule generation in dataset classification.
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