IMPROVED VARIABLE-LENGTH PARTICLE SWARM OPTIMIZATION FOR STRUCTURE-ADJUSTABLE EXTREME LEARNING MACHINE

Abstract

Extreme learning machine (ELM) is one of the single hidden layer feed-forward neural networks (SLFNs). It has been widely used for multiclass classification because of the preferable generalization performance and its faster learning speed. The parameters (including the input weights, hidden biases and the number of hidden neurons) have great impact on the generalization performance of ELM classifier. An improved variable-length particle swarm optimization (IVPSO) algorithm is proposed in this paper to automatically select the optimal structure of ELM classifier (the number of hidden neurons with the corresponding input weights and hidden biases) for maximizing the accuracy of validation data and minimizing the norm of output weights. It has been verified in the experimental results that the new algorithm IVPSO-ELM significantly increases the testing accuracy of many classification problems that we choose in UCI machine learning repository.