Improved Meta-ELM with error feedback incremental ELM as hidden nodes

Original Article


Liao et al. (Neurocomputing 128:81–87, 2014) proposed a meta-learning approach to extreme learning machine (Meta-ELM), which can obtain good generalization performance by training multiple ELMs. However, one of its open problems is overfitting when minimizing training error. In this paper, we propose an improved meta-learning model of ELM (improved Meta-ELM) to handle the problem. The improved Meta-ELM architecture is composed of some base ELMs which are error feedback incremental extreme learning machine (EFI-ELM) and the top ELM. The improved Meta-ELM includes two stages. First, each base ELM with EFI-ELM is trained on a subset of training data. Then, the top ELM learns with the base ELMs as hidden nodes. Simulation results on some artificial and benchmark datasets show that the proposed improved Meta-ELM model is more feasible and effective than Meta-ELM.


Meta-ELM Overfitting EFI-ELM Heterogeneous 


Compliance with ethical standards

Conflict of interest

The authors (Weidong Zou, Fenxi Yao, Baihai Zhang, Zixiao Guan) of paper (Title: Improved Meta-ELM with error feedback incremental ELM as hidden nodes, NCAA-D-16-00405-R2) declare that there is no conflict of interests.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Weidong Zou
    • 1
  • Fenxi Yao
    • 1
  • Baihai Zhang
    • 1
  • Zixiao Guan
    • 1
  1. 1.School of AutomationBeijing Institute of TechnologyBeijingPeople’s Republic of China

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