Architecture Selection of ELM Networks Based on Sensitivity of Hidden Nodes

Abstract

Extreme learning machine (ELM) was proposed as a new algorithm for training single-hidden layer feed-forward neural networks (SLFNs). One of the issues in EML is how to determine the architecture of SLFNs. Based on sensitivity of hidden nodes, an approach of architecture selection of ELM networks by applying a pruned method was proposed in this paper. The proposed pruning method utilizes sensitivity to measure the significance of hidden nodes. Beginning from an initial large number of hidden nodes, the insignificant nodes with lower sensitivity are then pruned. Experimental results on ten UCI data sets show that the proposed approach can obtain compact network architecture that generate comparable prediction accuracy on unseen samples.