Optimized combination of neural networks

Abstract

Parallel consensual neural networks (PCNN) are investigated. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. A non-linear combination method which utilizes a neural network is proposed and gives excellent results in experiments. The PCNN optimized with a neural network outperforms all other methods both in terms of training and test accuracies in the experiments.