Rule Extraction From Training Artificial Neural Network Using Variable Neighbourhood Search For Wisconsin Breast Cancer
Artificial neural network (ANN) may achieve successful classification value, but obtained results sometimes can be unintelligible and may not be interpreted. In literature, different rule extraction methods from trained ANN are applied to overcome this problem. This study presented a new method of extraction correct and intelligible rules from Trained ANN using variable neighbourhood search (VNS) metaheuristic method. Since the VNS method is independent from the ANN, it does not modify the results of ANN. Real time Wisconsin Breast Cancer (WBC) data was examined for determining the feasibility of the suggested method. The VNS method was used to obtain the best fitness values belong to input attributes, Id, which was maximized the fitness function Sr of output node r. The suggested method was the computational evaluated on investigated data sets and obtained results from the suggested approach indicated that had a potential to produce accurately and cored rules. The proposed method obtained accuracy value 98.97% for WBC dataset. Keywords variable neighbourhood search; artificial neural network; optimization