Improvements in Sugeno-Yasukawa Modelling Algorithm

Abstract

A modified version of Sugeno-Yasukawa (SY) modelling algorithm is presented. We have employed a new method for parameter identification phase based on genetic algorithms (GA). Moreover, we have modified the modeling sequence by applying parameter identification on intermediate models. Models created with this method had lower mean square errors (MSE) compared to original algorithm. A case study on breast cancer survival prediction is also presented that demonstrates a thorough comparison of the new modeling algorithm with several other methods such as SVM, C5 decision tree, ANFIS and the original SY method. The modified SY method had the highest average of accuracies among all models. Moreover, it had significantly higher accuracy compared to the original SY method and ANFIS. 10-fold cross validation approach was employed for all evaluations.