A new hybrid intelligent system for fast neural network training

Abstract

A major drawback of artificial neural network is long training time depending on a number of training data. Thus, the contribution of this work is to present the intelligent hybrid system for faster training on neural network. The concept of the proposed method is applying DBSCAN for removing noise and outliers then selecting the represented instances to form a smaller training set for further model training. The experimental results indicate that the proposed method can dramatically reduce a size of training set while the predictive performance of the classifiers are better or almost the same as models trained with original training sets. © 2013 Springer-Verlag Berlin Heidelberg.