Linear Combination of Neural Networks Using Particle Swarm Optimization

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 an ensemble system. In order to evaluate the effectiveness of the proposed method, we have carried out some experiments on two benchmark data sets: Satimage and Phoneme. Experimental results show that PSO-based weighting method outperforms the MSE and simple averaging methods