On the Use of Different Classification Rules in an Editing Task

  • Luisa Micó
  • Francisco Moreno-Seco
  • José Salvador Sánchez
  • José Martinez Sotoca
  • Ramón Alberto Mollineda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)

Abstract

Editing allows the selection of a representative subset of prototypes among the training sample to improve the performance of a classification task. The Wilson’s editing algorithm was the first proposal and then a great variety of new editing techniques have been proposed based on it. This algorithm consists on the elimination of prototypes in the training set that are misclassified using the k-NN rule. From such editing scheme, a general editing procedure can be straightforward derived, where any classifier beyond k-NN can be used. In this paper, we analyze the behavior of this general editing procedure combined with 3 different neighborhood-based classification rules, including k-NN. The results reveal better performances of the 2 other techniques with respect to k-NN in most of cases.

Keywords

Pattern recognition classification nearest neighbor prototype selection editing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luisa Micó
    • 1
  • Francisco Moreno-Seco
    • 1
  • José Salvador Sánchez
    • 2
  • José Martinez Sotoca
    • 2
  • Ramón Alberto Mollineda
    • 2
  1. 1.Dept. Llenguatges i Sistemes InformàticsUniversitat d’AlacantAlacantSpain
  2. 2.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume ICastelló de la PlanaSpain

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