A PSO-based weighting method for linear combination of neural networks
This paper presents a new way of computing the weights for combining multiple neural network classifiers based on particle swarm optimization, PSO. The weights are obtained so that they minimize the total classification error rate of the ensemble system. In order to evaluate the effectiveness of the proposed method, we have carried out some experiments on three data sets: 2-D normal, Satimage and Phoneme. Experimental results show that the PSO-based weighting method outperforms the MSE and simple averaging methods, especially for diverse networks.
neural networks particle swarm optimization ensemble system classifier combination