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Scalability Analysis of ANN Training Algorithms with Feature Selection

  • Verónica Bolón-Canedo
  • Diego Peteiro-Barral
  • Amparo Alonso-Betanzos
  • Bertha Guijarro-Berdiñas
  • Noelia Sánchez-Maroño
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7023)

Abstract

The advent of high dimensionality problems has brought new challenges for machine learning researchers, who are now interested not only in the accuracy but also in the scalability of algorithms. In this context, machine learning can take advantage of feature selection methods to deal with large-scale databases. Feature selection is able to reduce the temporal and spatial complexity of learning, turning an impracticable algorithm into a practical one. In this work, the influence of feature selection on the scalability of four of the most well-known training algorithms for feedforward artificial neural networks (ANNs) is studied. Six different measures are considered to evaluate scalability, allowing to establish a final score to compare the algorithms. Results show that including a feature selection step, ANNs algorithms perform much better in terms of scalability.

Keywords

Feature Selection Training Time Feature Subset Test Error Subset Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Verónica Bolón-Canedo
    • 1
  • Diego Peteiro-Barral
    • 1
  • Amparo Alonso-Betanzos
    • 1
  • Bertha Guijarro-Berdiñas
    • 1
  • Noelia Sánchez-Maroño
    • 1
  1. 1.Laboratory for Research and Development in Artificial Intelligence (LIDIA), Computer Science Dept.University of A CoruñaA CoruñaSpain

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