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A Preliminary Study of Diversity in Extreme Learning Machines Ensembles

  • Carlos Perales-GonzálezEmail author
  • Mariano Carbonero-Ruz
  • David Becerra-Alonso
  • Francisco Fernández-Navarro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10870)

Abstract

In this paper, the neural network version of Extreme Learning Machine (ELM) is used as a base learner for an ensemble meta-algorithm which promotes diversity explicitly in the ELM loss function. The cost function proposed encourages orthogonality (scalar product) in the parameter space. Other ensemble-based meta-algorithms from AdaBoost family are used for comparison purposes. Both accuracy and diversity presented in our proposal are competitive, thus reinforcing the idea of introducing diversity explicitly.

Keywords

Extreme learning machine Diversity Machine learning Ensemble AdaBoost 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Carlos Perales-González
    • 1
    Email author
  • Mariano Carbonero-Ruz
    • 1
  • David Becerra-Alonso
    • 1
  • Francisco Fernández-Navarro
    • 1
  1. 1.Department of Quantitative MethodsUniversidad Loyola AndaluciaSevillaSpain

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