Genetic Algorithm based Selective Neural Network Ensemble

Abstract

Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual networks in some cases. Therefore an approach named G ASEN is proposed, which trains several individual neural networks and then employs genetic algorithm to select an optimum subset of individual networks to constitute an ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e. averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, G ASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost.