Abstract:

To slove the network size control problems of the extremely learning machine (ELM), we propose an ELM classification algorithm based on the  influence degree pruning. The algorithm uses the individual ELM hidden node which connects the input and output layer weight vector, the output of the node, the number of samples and the initial number of hidden nodes to define the influence degree of the hidden node on the entire network. Then the importance of the hidden node is determined by the sorted influence degree, and the pruning step length which matches the ELM network scale is used to delete redundant nodes. Finally the weight vectors are updated. We categorize several practical problems on UCI data sets through experiments, and compare the proposed algorithm with the EMELM, PELM and ELM. Experimental results show that the proposed algorithm has higher stability and precision and faster training speed, and it can control the network size effectively. 

Key words: ELM;analysis of influence degree;pruning algorithm;network size;classification algorithm