Training Product-Unit Neural Networks with Cuckoo Optimization Algorithm for Classification

In this study Product-Unit Neural Networks (PUNN) which is the special classof feed-forward neural networkhas been trained using Cuckoo Optimization algorithm. The trained model has been applied to two classification problems. BUPA liver disorders and Haberman's Survival Data have been used for application. The both data have been obtained from UCI machine Learning Repository. For comparison,Backpropagation (BP) and Levenberg–Marquardt (LM) algorithms have been used. Classification accuracy, sensitivity, specificity and F1 score have been used as statistics to evaluate the success of algorithms. The application results show that the PUNN trained with Cuckoo Optimizationalgorithm is achieved betterclassification accuracy, sensitivity, specificity and F1 score

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