In this paper, a genetic neural network classification model is developed. The proposed model can not only optimize the weights and thresholds of neural network, but also reduce network size by identifying the feature subset effectively using genetic algorithm. This technique is aimed at finding out a network that is large enough to capture the accurate class attributes of the data as much as possible, while retaining the generalization capability of neural network. Breast cancer datasets (BCD) in UCI Machine Learning Repository are utilized to evaluate the proposed genetic neural network approach in this paper. Simulation results show that the developed model achieved dimensional reduction and the identification of benign and malignant tumors. Accordingly, it improved classification accuracy and demonstrated excellent classification efficiency.
['Yaoying Huang', 'Wanggen Li', 'Xiaojiao Ye']
A Study of Genetic Neural Network as Classifiers and its Application in Breast Cancer Diagnosis