Abstract
This Paper Proposes a Combination of Methodologies based on a Recent Development -Called Extreme Learning Machine (Elm)- Decreasing Drastically the Training Time of Nonlinear Models. Variable Selection is Beforehand Performed on the Original Dataset, using the Partial Least Squares (Pls) and a Projection based on Nonparametric Noise Estimation (NNE), to Ensure Proper Results by the Elm Method. Then, after the Network is First Created using the Original Elm, the Selection of the Most Relevant Nodes is Performed by using a Least Angle Regression (Lars) Ranking of the Nodes and a Leave-One-Out Estimation of the Performances, Leading to an Optimally Pruned Elm (Op-Elm). Finally, the Prediction Accuracy of the Global Methodology is Demonstrated using the ESTSP 2008 Competition and Poland Electricity Load Datasets. ©2008 IEEE.
Recommended Citation
A. Sorjamaa et al., "Long-Term Prediction of Time Series using NNE-Based Projection and OP-ELM," Proceedings of the International Joint Conference on Neural Networks, pp. 2674 - 2680, article no. 4634173, Institute of Electrical and Electronics Engineers, Nov 2008.
The definitive version is available at https://doi.org/10.1109/IJCNN.2008.4634173
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142441821-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
24 Nov 2008
