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Improving the Performance of the RBF Neural Networks Trained with Imbalanced Samples

  • R. Alejo
  • V. García
  • J. M. Sotoca
  • R. A. Mollineda
  • J. S. Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

Recently, the class imbalance problem in neural networks, is receiving growing attention in works of machine learning and data mining. This problem appears when the samples of some classes are much smaller than those in the other classes. The classes with small size can be ignored in the learning process and the convergence of these classes is very slow. This paper studies empirically the class imbalance problem in the context of the RBF neural network trained with backpropagation algorithm. We propose to introduce a cost function in the training process to compensate imbalance class and one strategy to reduce the impact of the cost function in the data probability distribution.

Keywords

Cost Function Mean Square Error Minority Class Class Imbalance Imbalance Problem 
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 2007

Authors and Affiliations

  • R. Alejo
    • 1
    • 2
  • V. García
    • 1
    • 2
  • J. M. Sotoca
    • 1
  • R. A. Mollineda
    • 1
  • J. S. Sánchez
    • 1
  1. 1.Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la PlanaSpain
  2. 2.Lab. de Reconocimiento de Patrones, Instituto Tecnológico de Toluca, Av. Tecnológico S/N, 52140, MetepecMexico

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