OP-ELM

OP-ELM: optimally pruned extreme learning machine. In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems. Results for both computational time and accuracy (mean square error) are compared to the original ELM and to three other widely used methodologies: multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate, the proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM. Despite the simplicity and fast performance, the OP-ELM is still able to maintain an accuracy that is comparable to the performance of the SVM. A toolbox for the OP-ELM is publicly available online.


References in zbMATH (referenced in 12 articles )

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  1. Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou: Trends in extreme learning machines: a review (2015)
  2. Fink, Olga; Zio, Enrico; Weidmann, Ulrich: Quantifying the reliability of fault classifiers (2014) ioport
  3. Grigorievskiy, Alexander; Miche, Yoan; Ventelä, Anne-Mari; Séverin, Eric; Lendasse, Amaury: Long-term time series prediction using OP-ELM (2014)
  4. Augasta, M.Gethsiyal; Kathirvalavakumar, T.: Pruning algorithms of neural networks -- a comparative study (2013) ioport
  5. Bueno-Crespo, Andrés; García-Laencina, Pedro J.; Sancho-Gómez, José-Luis: Neural architecture design based on extreme learning machine (2013) ioport
  6. Cao, Jiuwen; Lin, Zhiping; Huang, Guang-Bin; Liu, Nan: Voting based extreme learning machine (2012) ioport
  7. Romero, Enrique; Alquézar, René: Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks (2012) ioport
  8. Wang, Gai-tang; Li, Ping; Cao, Jiang-tao: Variable activation function extreme learning machine based on residual prediction compensation (2012) ioport
  9. Wang, Ran; Kwong, Sam; Wang, Xizhao: A study on random weights between input and hidden layers in extreme learning machine (2012) ioport
  10. Zhao, Jianwei; Park, Dong Sun; Lee, Joonwhoan; Cao, Feilong: Generalized extreme learning machine acting on a metric space (2012)
  11. Pouzols, Federico Montesino; Lendasse, Amaury; Barros, Angel Barriga: Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation (2010) ioport
  12. Miche, Yoan; Sorjamaa, Antti; Lendasse, Amaury: OP-ELM: Theory, experiments and a toolbox (2008) ioport