Fuzzy modeling using Bat Algorithm optimization for classification - Noor Amidah, Ahmad Sultan (2018)
Fuzzy modeling is a process of generating parameters which are fuzzy rule and membership function. Fuzzy rule is a form of a fuzzy condition and membership function is a generality of indicator function in classical sets. In order to create parameters, there are many problems arise in the process of fuzzy modeling. The problems are data incomplete and the size of the data is large. Data incomplete is happened when some of data to process is missing so it failed to record it. Problem of the size of data happen when data cannot be process because of it too complex. In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. The study of Bat Algorithm is achieved the purpose of this research. The purpose of this research is to apply and evaluate the performance of Bat Algorithm for classification. In order conduct an experiment in this research, there are several dataset is use which are WBCD dataset, Haberman’s Survival dataset and Pima Indian dataset. A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. The value of parameter is already set to use when applying every dataset in an experiment. As a result, the highest average accuracy generated which is 96.91% by using WBCD dataset. The average accuracy of Bat Algorithm is comparing with other methods. Every dataset is producing the best fuzzy rule and membership function. Overall of this research, the objectives of this research is achieved.