Cost-Sensitive Neural Networks and Editing Techniques for Imbalance Problems

  • R. Alejo
  • J. M. Sotoca
  • V. García
  • R. M. Valdovinos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

The multi-class imbalance problem in supervised pattern recognition methods is receiving growing attention. Imbalanced datasets means that some classes are represented by a large number of samples while the others classes only contain a few. In real-world applications, imbalanced training sets may produce an important deterioration of the classifier performance when neural networks are applied in the classes less represented. In this paper we propose training cost-sentitive neural networks with editing techniques for handling the class imbalance problem on multi-class datasets. The aim is to remove majority samples while compensating the class imbalance during the training process. Experiments with real data sets demonstrate the effectiveness of the strategy here proposed.

Keywords

Multi-class imbalance backpropagation cost function editing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • R. Alejo
    • 1
  • J. M. Sotoca
    • 1
  • V. García
    • 1
  • R. M. Valdovinos
    • 2
  1. 1.Institute of New Imaging Technologies Dept. Llenguatges i Sistemes InformáticsUniversitat Jaume ICastelló de la PlanaSpain
  2. 2.Centro Universitario UAEM Valle de ChalcoUniversidad Autónoma del Estado de MéxicoValle de ChalcoMexico

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