Dynamic Integration Approach for an Ensemble of Neural Classifiers

A dynamic integration approach for an ensemble of neural classifiers(NCs) was presented in this paper.It established different NCs based on bootstrapping technique,and evaluated the classification accuracy of every NC by different sorts of weighted nearest neighbors for mixed attributes,then the NCs with low relative generalization error rates were dynamically selected and majority voting was applied to those NCs in order to conduct the final classification results of the ensemble.This approach was compared with some integration approaches on classification performance for ten data sets from UCI.The experiments showed that this approach could obtain the best average classification accuracy over all those data sets.At the same time,it is easy to see that Bagging is better than the method with different number of hidden units(MDHU) for generating different NCs, and the performance of the ensemble may not be improved by combining Bagging with MDHU.