Object Selection Based on Clustering and Border Objects

  • J. Arturo Olvera-López
  • J. Ariel Carrasco-Ochoa
  • J. Francisco Martínez-Trinidad
Part of the Advances in Soft Computing book series (AINSC, volume 45)


Object selection is an important task for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new method based on clustering which tries to find border objects that contribute with useful information allowing to the classifier discriminating between classes. An experimental comparison of our method, the CLU method based on clustering, and the DROP methods, is presented.


Drop Method Central Object Minority Class Original Training Frequent Class 
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 2007

Authors and Affiliations

  • J. Arturo Olvera-López
    • 1
  • J. Ariel Carrasco-Ochoa
    • 1
  • J. Francisco Martínez-Trinidad
    • 1
  1. 1.National Institute of Astrophysics, Optics and ElectronicsPueblaMexico

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