Quick Hidden Layer Size Tuning in ELM for Classification Problems

  • Audi Albtoush Faculty of Computer Science and Information Technology, Jerash University, Jordan
  • Manuel Fernandez-Delgado Santiago De Compostela University, Spain
  • Haitham Maarouf Santiago De Compostela University, Spain
  • Asmaa Jameel Al Nawaiseh Software Engineering Department, Mut'ah university, Jordan
Keywords: Extreme Learning Machine, Number of Hidden Nodes, Moving Average, Exponential Moving Average, Divide-and-conquer


The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98\% with equal or better generalization ability.


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How to Cite
Albtoush, A., Fernandez-Delgado, M., Maarouf, H. and Al Nawaiseh, A. 2024. Quick Hidden Layer Size Tuning in ELM for Classification Problems. MENDEL. 30, 1 (Jun. 2024), 1-14. DOI:https://doi.org/10.13164/mendel.2024.1.001.
Research articles