Ensemble learning has become one of research fields of machine learning

it dramatically improves generalization performance of classifier.After analyzing ensemble approach to both Bagging and Adaboost

we point out their some flaws.Then we present a novel approach to neural network ensemble

called DBNNE below.In this method

a diverse data set is generated to increase ensemble diversity.Moreover

to ensure high accuracy of ensemble

we test performance of ensemble when a classifier is added to ensemble .Finally

we experiment on ten representative data sets.The results show that DBNNE achieves higher predictive accuracy than Bagging and AdaBoost on small data sets and comparable performance on larger data sets.