Extreme learning machine: a robust modeling technique? yes!

Authors:
Amaury Lendasse;Anton Akusok;Olli Simula;Francesco Corona;Mark van Heeswijk;Emil Eirola;Yoan Miche
Affiliations:
Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland,Basque Foundation for Science, IKERBASQUE, Bilbao, Spain,Computational Intelligence Group, Compu ...;Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland;Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland;Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland;Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland;Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland;Information and Computer Science Department, Aalto School of Science and Technology, Aalto, Finland
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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Abstract

In this paper is described the original (basic) Extreme Learning Machine (ELM). Properties like robustness and sensitivity to variable selection are studied. Several extensions of the original ELM are then presented and compared. Firstly, Tikhonov-Regularized Optimally-Pruned Extreme Learning Machine (TROP-ELM) is summarized as an improvement of the Optimally-Pruned Extreme Learning Machine (OP-ELM) in the form of a L2 regularization penalty applied within the OP-ELM. Secondly, a Methodology to Linearly Ensemble ELM (ELM-ELM) is presented in order to improve the performance of the original ELM. These methodologies (TROP-ELM and ELM-ELM) are tested against state of the art methods such as Support Vector Machines or Gaussian Processes and the original ELM and OP-ELM, on ten different data sets. A specific experiment to test the sensitivity of these methodologies to variable selection is also presented.