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An improved particle swarm optimization(PSO) algorithm coupling with prior information for classification of large scale dataset is proposed in this paper. The prior information derived from the data set is used to determine the initialposition of the particles. In the new algorithm, neural network is first trained by improved PSO and then by back-propagation (BP). The prior information narrows the search space and guides the movement direction of the particles, so the convergence rate and the generalization performance are improved. Experimental results demonstrate that the new algorithm is more effective than traditional methods.

An improved PSO algorithm coupling with prior information for classification of large scale dataset