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.

翻译：搜索空间日益增长的复杂性和维度会对模糊规则分类器的归纳学习产生不利影响，从而制约模糊系统的可扩展性与分类精度。本研究针对糖尿病分类这一具体挑战，采用脑风暴优化算法，提出了一种重新定义规则生成机制的新型模糊系统。通过将指数模型集成至标准BSO算法，我们增强了针对糖尿病相关数据的规则推导能力。实验结果表明，该创新模糊系统应用于糖尿病数据集分类任务时，显著提升了分类准确率。