用脑风暴优化算法提升糖尿病分类的模糊规则系统
Improving Fuzzy Rule Classifier with Brain Storm Optimization and Rule Modification
- 引入改进型脑风暴优化算法生成模糊规则
- 在糖尿病数据集上准确率显著提升
- 适合医疗诊断类模糊系统研究者参考
随着搜索空间复杂度与维度增加，模糊规则分类器的归纳学习能力受到负面影响，进而制约模糊系统的可扩展性与准确性。本研究针对糖尿病分类问题，采用脑风暴优化（BSO）算法，提出一种新型模糊系统，重新定义该场景下的规则生成机制。通过在标准BSO算法中融入指数模型，优化规则推导过程，特别适配糖尿病相关数据特征。所提出的创新模糊系统在多个糖尿病数据集上进行分类实验，结果表明分类准确率获得显著提升。
原文摘要 · Abstract (English)
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.
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