An Approach to Improving Diversity of Neural Network Ensemble
Kai Li, Huang Houkuan · Dianzi xuebao · 2005
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.