Reducing Training Sets by NCN-based Exploratory Procedures

  • M. Lozano
  • José S. Sánchez
  • Filiberto Pla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2652)


In this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques.


Near Neighbour Pattern Recognition Letter Prototype Selection Prototype Selection Method Lower Reduction Percentage 
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 2003

Authors and Affiliations

  • M. Lozano
    • 1
  • José S. Sánchez
    • 1
  • Filiberto Pla
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosUniversitat Jaume ICastellónSpain

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