MIMO CMAC neural network classifier for solving classification problems

Abstract

Developing an efficient classification method is a challenge task in many research domains, such as neural network (NN) classifiers, statistical classifiers and machine learning. This study focuses on NN classifiers, which are data-driven analytical techniques. This study presents a cerebellar model articulation controller NN (CMAC NN) classifier, which has the advantages of very fast learning, reasonable generalization ability and robust noise resistance. To increase the accuracies of training and generalization, the CMAC NN classifier is designed with multiple-input and multiple-output (MIMO) network topology. The performance of the proposed MIMO CMAC NN classifier is evaluated using PROBEN1 benchmark datasets (such as for diabetes, cancer and glass) taken from the UCI Machine Learning Repository. Numerical results indicate that the proposed CMAC NN classifier is efficient for tested datasets. Moreover, this study compares the experimental results of the CMAC NN classifier with those in the published literature, indicating that the CMAC NN classifier is superior to some published classifiers. Therefore, the CMAC NN classifier can be considered as an analytical tool for solving classification tasks, such as medical decision making.