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Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers

  • Milton García-Borroto
  • Yenny Villuendas-Rey
  • Jesús Ariel Carrasco-Ochoa
  • José Fco. Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

The Nearest Neighbor classifier is a simple but powerful non-parametric technique for supervised classification. However, it is very sensitive to noise and outliers, which could decrease the classifier accuracy. To overcome this problem, we propose two new editing methods based on maximum similarity graphs. Numerical experiments in several databases show the high quality performance of our methods according to classifier accuracy.

Keywords

nearest neighbor error-based editing prototype selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Milton García-Borroto
    • 1
    • 3
  • Yenny Villuendas-Rey
    • 2
  • Jesús Ariel Carrasco-Ochoa
    • 3
  • José Fco. Martínez-Trinidad
    • 3
  1. 1.Bioplantas CenterUNICAC. de ÁvilaCuba
  2. 2.Ciego de Ávila University UNICAC. de ÁvilaCuba
  3. 3.National Institute of Astrophysics,Optics and ElectronicsPueblaMéxico

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