Ensemble of GA based Selective Neural Network Ensembles

Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, e-GASEN, a twolayer neural network ensemble architecture is proposed, in which the base learners of the final ensemble are also ensembles. Experimental results show that e-GASEN generalizes better than a popular ensemble method. The reason why e-GASEN works is also discussed. We believe that the different layers of e-GASEN attain good generalization ability for different reasons. The first layer ensembles profit from the selected individual neural networks that are moderately divergent but generalize well, while the second layer ensemble profits from the divergency among the first layer ensembles.