A methodology for Building Regression Models using Extreme Learning Machine: OP-ELM
Yoan Miche, Patrick Bas, Christian Jutten, Olli Simula and Amaury Lendasse
In: ESANN 2008, European Symposium on Artificial Neural Networks, April 23-25 2008, Bruges, Belgium.
This paper proposes a methodology named OP-ELM, based
on a recent development –the Extreme Learning Machine– decreasing drastically the training speed of networks. Variable selection is beforehand
performed on the original dataset for proper results by OP-ELM: the net-
work is ﬁrst created using Extreme Learning Process, selection of the most relevant nodes is performed using Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances. Results are globally equivalent to LSSVM ones with reduced computational time.