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Stochastic Sensitivity Analysis Using Extreme Learning Machine

  • David Becerra-Alonso
  • Mariano Carbonero-Ruz
  • Alfonso Carlos Martínez-Estudillo
  • Francisco José Marténez-Estudillo
Chapter
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 16)

Abstract

The Extreme Learning Machine classifier is used to perform the perturbative method known as Sensitivity Analysis. The method returns a measure of class sensitivity per attribute. The results show a strong consistency for classifiers with different random input weights. In order to present the results obtained in an intuitive way, two forms of representation are proposed and contrasted against each other. The relevance of both attributes and classes is discussed. Class stability and the ease with which a pattern can be correctly classified are inferred from the results. The method can be used with any classifier that can be replicated with different random seeds.

Keywords

Extreme learning machine Sensitivity analysis ELM feature space ELM solutions space Classification Stochastic classifiers 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Becerra-Alonso
    • 1
  • Mariano Carbonero-Ruz
    • 1
  • Alfonso Carlos Martínez-Estudillo
    • 1
  • Francisco José Marténez-Estudillo
    • 1
  1. 1.Department of Management and Quantitative Methods, AYRNA Research GroupUniversidad Loyola AndalucíaCórdobaSpain

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