BSFS: Design and Development of Exponential Brain Storm Fuzzy System for Data Classification

Abstract

The inductive learning of fuzzy rule classifier suffers in the rule generation and rule optimization when the search space or variables becomes high. This creates the new idea of making the fuzzy system with precise rules leading to less scalability and improved accuracy. Accordingly, different approaches have been presented in the literature for optimal finding of fuzzy rules using optimization algorithms. Here, we make use of the brain storm optimization algorithm for rule optimization. In this paper, a new fuzzy system called, exponential brain storm fuzzy system is developed by modifying the traditional fuzzy system in rule definition process. In rule derivation, we have presented an algorithm called, EBSO by modifying the BSO algorithm with exponential model. Also, the membership function is designed using simple uniform distribution-based approach. Finally, data classification is performed with a new BSFS system using three medical databases such as, PID, Cleveland and DRD. The experimentation proved that the proposed BSFS clearly outperformed in all the three datasets by reaching the maximum accuracy.