Ensemble strategies to build neural network to facilitate decision making

There are three major strategies to form neural network ensembles. The simplest one is the Cross Validation strategy in which all members are trained with the same training data. Bagging and boosting strategies produce perturbed sample from training data. This paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based upon these factors, it compares the results of the trained single model with the cross validation one in a case which uses the presidential election data in US. The trained single model is called single best model. In this experience, the comparison shows that the cross validation ensemble leads to lower generalization error.