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Improving the Classification Accuracy of RBF and MLP Neural Networks Trained with Imbalanced Samples

  • R. Alejo
  • V. Garcia
  • J. M. Sotoca
  • R. A. Mollineda
  • J. S. Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

In practice, numerous applications exist where the data are imbalanced. It supposes a damage in the performance of the classifier. In this paper, an appropriate metric for imbalanced data is applied as a filtering technique in the context of Nearest Neighbor rule, to improve the classification accuracy in RBF and MLP neural networks. We diminish atypical or noisy patterns of the majority-class keeping all samples of the minority-class. Several experiments with these preprocessing techniques are performed in the context of RBF and MLP neural networks.

Keywords

Radial Basis Function Near Neighbor Radial Basis Function Neural Network Weighted Distance Minority Class 
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 2006

Authors and Affiliations

  • R. Alejo
    • 1
    • 2
  • V. Garcia
    • 1
    • 2
  • J. M. Sotoca
    • 1
  • R. A. Mollineda
    • 1
  • J. S. Sánchez
    • 1
  1. 1.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume ICastelló de la PlanaSpain
  2. 2.Instituto Tecnológico de TolucaLab. de Reconocimiento de PatronesMetepecMéxico

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