Abstract
Neural network ensemble (NNE) is a collection of a finite number of networks that are trained for a task to achieve better performance. A number of NNE methods are proposed and is shown effective over single network. The proposed methods apply different techniques for NNE construction and a method is shown effective for a subset of problems. In another word, no method is found that is superior to others for all the problems. Keeping it in front, in this study we investigated an approach to construct an NNE that might effective for all the problems. For NNE construction first we trained sets of networks with different NNE methods and then select networks for final NNE. The proposed technique has been tested extensively on several benchmark problems of machine learning and neural networks. Experimental results show that this method able to construct concise NNE and outperformed other individual methods.
