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Mixed Data Object Selection Based on Clustering and Border Objects

  • J. Arturo Olvera-López
  • J. Francisco Martínez-Trinidad
  • J. Ariel Carrasco-Ochoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

In supervised classification, the object selection or instance selection is an important task, mainly 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 mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers.

Keywords

Supervised Classifiers Object Selection Clustering Mixed Data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • J. Arturo Olvera-López
    • 1
  • J. Francisco Martínez-Trinidad
    • 1
  • J. Ariel Carrasco-Ochoa
    • 1
  1. 1.Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840Mexico

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