Improving Fuzzy Rule Classifier with Brain Storm Optimization and Rule Modification

TL;DR

This paper introduces a novel fuzzy rule classifier enhanced with Brain Storm Optimization and rule modification, specifically designed for diabetic data, resulting in improved classification accuracy and scalability.

Contribution

It presents a new fuzzy system integrating BSO with an exponential model for better rule generation in diabetic classification tasks.

Findings

Significant accuracy improvement on diabetic datasets

Effective rule generation using BSO with exponential model

Enhanced scalability in fuzzy rule classification

Abstract

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.