The Imbalanced Training Sample Problem: Under or over Sampling?

  • Ricardo Barandela
  • Rosa M. Valdovinos
  • J. Salvador Sánchez
  • Francesc J. Ferri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


The problem of imbalanced training sets in supervised pattern recognition methods is receiving growing attention. Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. It has been observed that this situation, which arises in several practical domains, may produce an important deterioration of the classification accuracy, in particular with patterns belonging to the less represented classes. In this paper we present a study concerning the relative merits of several re-sizing techniques for handling the imbalance issue. We assess also the convenience of combining some of these techniques.


Majority Class Weighted Distance Under Sampling Minority Class Neighbor Rule 
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 2004

Authors and Affiliations

  • Ricardo Barandela
    • 1
    • 2
  • Rosa M. Valdovinos
    • 1
  • J. Salvador Sánchez
    • 3
  • Francesc J. Ferri
    • 4
  1. 1.Instituto Tecnológico de TolucaMetepecMéxico
  2. 2.Instituto de Geografía TropicalLa HabanaCuba
  3. 3.Dept. Llenguatges i Sistemes InformàticsU. Jaume ICastellóSpain
  4. 4.Dept. d’InformàticaU. ValenciaBurjassot (Valencia)Spain

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