An Empirical Study for the Multi-class Imbalance Problem with Neural Networks

  • R. Alejo
  • J. M. Sotoca
  • G. A. Casañ
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


The latest research in neural networks demonstrates that the class imbalance problem is a critical factor in the classifiers performance when working with multi-class datasets. This occurs when the number of samples of some classes is much smaller compared to other classes. In this work, four different options to reduce the influence of the class imbalance problem in the neural networks are studied. These options consist of introducing several cost functions in the learning algorithm in order to improve the generalization ability of the networks and speed up the convergence process.


Multi-class imbalance backpropagation cost function 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • R. Alejo
    • 1
    • 2
    • 3
  • J. M. Sotoca
    • 3
  • G. A. Casañ
    • 3
  1. 1.Centro Universitario UAEM Atlacomulco, Universidad Autónoma del Estado de MéxicoAtlacomulcoMéxico
  2. 2.Lab. Reconocimiento de Patrones, Instituto Tecnológico de TolucaMetepecMéxico
  3. 3.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume ICastelló de la PlanaSpain

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