Pruning algorithm for extreme learning machine based on sensitivity analysis
LI Fan-ju · Kongzhi yu juece · 2014
In order to design the structure of extreme learning machine(ELM), a pruning algorithm is proposed by using the sensitivity analysis method. The residual error's sensitivities to the hidden nodes are defined by their outputs and weight vectors connecting to the output layer. The model scale adaptability is calculated and the hidden nodes are sorted by using the defined sensitivities. Then, the number of requisite hidden nodes is estimated by the model scale adaptability. The redundant nodes with smaller sensitivities are removed from the existent network. The simulation results show that the proposed approach can construct the compact structure for ELM effectively.