An Empirical Study of Oversampling and Undersampling Methods for LCMine an Emerging Pattern Based Classifier

  • Octavio Loyola-González
  • Milton García-Borroto
  • Miguel Angel Medina-Pérez
  • José Fco. Martínez-Trinidad
  • Jesús Ariel Carrasco-Ochoa
  • Guillermo De Ita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


Classifiers based on emerging patterns are usually more understandable for humans than those based on more complex mathematical models. However, most of the classifiers based on emerging patterns get low accuracy in those problems with imbalanced databases. This problem has been tackled through oversampling or undersampling methods, nevertheless, to the best of our knowledge these methods have not been tested for classifiers based on emerging patterns. Therefore, in this paper, we present an empirical study about the use of oversampling and undersampling methods to improve the accuracy of a classifier based on emerging patterns. We apply the most popular oversampling and undersampling methods over 30 databases from the UCI Repository of Machine Learning. Our experimental results show that using oversampling and undersampling methods significantly improves the accuracy of the classifier for the minority class.


supervised classification emerging patterns imbalanced databases oversampling undersampling 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Octavio Loyola-González
    • 1
    • 2
  • Milton García-Borroto
    • 1
  • Miguel Angel Medina-Pérez
    • 2
  • José Fco. Martínez-Trinidad
    • 2
  • Jesús Ariel Carrasco-Ochoa
    • 2
  • Guillermo De Ita
    • 3
  1. 1.Centro de BioplantasUniversidad de Ciego de Ávila.Ciego de ÁvilaCuba
  2. 2.Instituto Nacional de Astrofísica, Óptica y ElectrónicaSta. María TonanzintlaMéxico
  3. 3.Faculty of Computer ScienceBenemérita Universidad Autónoma de PueblaMéxico

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