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 22 articles )

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  1. Wang, Kuaini; Cao, Jinde; Pei, Huimin: Robust extreme learning machine in the presence of outliers by iterative reweighted algorithm (2020)
  2. Wang, Kuaini; Pei, Huimin; Cao, Jinde; Zhong, Ping: Robust regularized extreme learning machine for regression with non-convex loss function via DC program (2020)
  3. Chen, Chuangquan; Vong, Chi-Man; Wong, Chi-Man; Wang, Weiru; Wong, Pak-Kin: Efficient extreme learning machine via very sparse random projection (2018)
  4. Roshan, Setareh; Miche, Yoan; Akusok, Anton; Lendasse, Amaury: Adaptive and online network intrusion detection system using clustering and extreme learning machines (2018)
  5. Mei, Ying; Tan, Guanzheng; Liu, Zhentao: An improved brain-inspired emotional learning algorithm for fast classification (2017)
  6. Zhao, Yong-Ping: Parsimonious kernel extreme learning machine in primal via Cholesky factorization (2016)
  7. Cao, Jiuwen; Lin, Zhiping: Extreme learning machines on high dimensional and large data applications: a survey (2015)
  8. Ding, Shifei; Zhang, Nan; Xu, Xinzheng; Guo, Lili; Zhang, Jian: Deep extreme learning machine and its application in EEG classification (2015)
  9. Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou: Trends in extreme learning machines: a review (2015)
  10. Zhang, Yanjun; Li, Tie; Na, Guangyu; Li, Guoqing; Li, Yang: Optimized extreme learning machine for power system transient stability prediction using synchrophasors (2015)
  11. Fink, Olga; Zio, Enrico; Weidmann, Ulrich: Quantifying the reliability of fault classifiers (2014) ioport
  12. Grigorievskiy, Alexander; Miche, Yoan; Ventelä, Anne-Mari; Séverin, Eric; Lendasse, Amaury: Long-term time series prediction using OP-ELM (2014)
  13. Liu, Nan; Cao, Jiuwen; Koh, Zhi Xiong; Pek, Pin Pin; Ong, Marcus Eng Hock: Risk stratification with extreme learning machine: a retrospective study on emergency department patients (2014)
  14. Augasta, M. Gethsiyal; Kathirvalavakumar, T.: Pruning algorithms of neural networks -- a comparative study (2013) ioport
  15. Bueno-Crespo, Andrés; García-Laencina, Pedro J.; Sancho-Gómez, José-Luis: Neural architecture design based on extreme learning machine (2013) ioport
  16. Cao, Jiuwen; Lin, Zhiping; Huang, Guang-Bin; Liu, Nan: Voting based extreme learning machine (2012) ioport
  17. Romero, Enrique; Alquézar, René: Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks (2012) ioport
  18. Wang, Gai-tang; Li, Ping; Cao, Jiang-tao: Variable activation function extreme learning machine based on residual prediction compensation (2012) ioport
  19. Wang, Ran; Kwong, Sam; Wang, Xizhao: A study on random weights between input and hidden layers in extreme learning machine (2012) ioport
  20. Zhao, Jianwei; Park, Dong Sun; Lee, Joonwhoan; Cao, Feilong: Generalized extreme learning machine acting on a metric space (2012)

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