A variable selection approach based on the Delta Test for Extreme Learning Machine models

Extreme Learning Machine, ELM, is a newly available learning algorithm for single layer feedforward neural networks (SLFNs), and it has proved to show the best compromise between learning speed and accuracy of the estimations. In this paper, a methodology based on Optimal-Pruned ELM (OP-ELM) for function approximation enhanced with variable selection using the Delta Test is introduced. The least angle regression (LARS) algorithm is used after variable selection to rank the input variables, and scaling is also introduced as a way to estimate the influence of each input in the output value. The performance is assessed on a dataset related to anthropometric measurements for children weight prediction. The accurate results show that this combination of techniques is very promising to solve real world problems and represents a good alternative to classic backpropagation methods.