A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Sets

  • M. Karagiannopoulos
  • D. Anyfantis
  • S. Kotsiantis
  • P. Pintelas
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)


A classifier induced from an imbalanced data set has a low error rate for the majority class and an undesirable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed wrapper for reweighting training instances and it concludes that such a framework can be a more valuable solution to the problem.


Base Classifier Threshold Method Training Instance Minority Class Positive Instance 
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

© International Federation for Information Processing 2007

Authors and Affiliations

  • M. Karagiannopoulos
    • 1
  • D. Anyfantis
    • 1
  • S. Kotsiantis
    • 1
  • P. Pintelas
    • 1
  1. 1.Educational Software Development Laboratory, Department of MathematicsUniversity of PatrasGreece

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