Optimizing configuration of neural ensemble network for breast cancer diagnosis

Determining the best values for the parameters of a classifier is a challenge. This challenge is compounded for ensembles. This research evaluates the number of neurons for candidate networks and the number of committee members in our work on variable neural classifiers for breast cancer diagnosis. The evaluation reveals that good neural network accuracy can be achieved with a small number of neurons in the hidden layer and three committee members in the ensemble. The proposed methodology is tested on two benchmark databases achieving 99% classification accuracy.