Edition Schemes Based on BSE

  • J. Arturo Olvera-López
  • J. Fco. Martínez-Trinidad
  • J. Ariel Carrasco-Ochoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


Edition is an important and useful task in supervised classification specifically for instance-based classifiers because edition discards from the training set those useless or harmful objects for the classification accuracy and it helps to reduce the size of the original training sample and to increase both the classification speed and accuracy. In this paper, we propose two edition schemes that combine edition methods and sequential search for instance selection. In addition, we present an empirical comparison between these schemes and some other edition methods.


Classification Accuracy Noise Filter Neural Network Ensemble Edition Method Edition Scheme 
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 2005

Authors and Affiliations

  • J. Arturo Olvera-López
    • 1
  • J. Fco. Martínez-Trinidad
    • 1
  • J. Ariel Carrasco-Ochoa
    • 1
  1. 1.Computer Science DepartmentNational Institute of Astrophysics, Optics and ElectronicsSta. María TonantzintlaMéxico

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