Prototype Selection Via Prototype Relevance

  • J. Arturo Olvera-López
  • J. Ariel Carrasco-Ochoa
  • J. Fco. Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


In Pattern recognition, the supervised classifiers use a training set T for classifying new prototypes. In practice, not all information in T is useful for classification therefore it is necessary to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time in the training and/or classification stages could be reduced. Several prototype selection methods have been proposed following the Nearest Neighbor (NN) rule; in this work, we propose a new prototype selection method based on the prototype relevance and border prototypes, which is faster (over large datasets) than the other tested prototype selection methods. We report experimental results showing the effectiveness of our method and compare accuracy and runtimes against other prototype selection methods.


Prototype selection border prototypes supervised classification data reduction 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

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

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