Improving Fuzzy Rule Classifier with Brain Storm Optimization and Rule Modification

Autor:	Huang, Yan, Liu, Wei, Zang, Xiaogang
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Neural and Evolutionary Computing I.2.7
Druh dokumentu:	Working Paper
Popis:	The expanding complexity and dimensionality in the search space can adversely affect inductive learning in fuzzy rule classifiers, thus impacting the scalability and accuracy of fuzzy systems. This research specifically addresses the challenge of diabetic classification by employing the Brain Storm Optimization (BSO) algorithm to propose a novel fuzzy system that redefines rule generation for this context. An exponential model is integrated into the standard BSO algorithm to enhance rule derivation, tailored specifically for diabetes-related data. The innovative fuzzy system is then applied to classification tasks involving diabetic datasets, demonstrating a substantial improvement in classification accuracy, as evidenced by our experiments. Comment: 9 pages,8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.01413 Zobrazit plný text záznamu View this record from Arxiv