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On Filtering the Training Prototypes in Nearest Neighbour Classification

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Topics in Artificial Intelligence (CCIA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2504))

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Abstract

Filtering (or editing) is mainly effective in improving the classification accuracy of the Nearest Neighbour (NN) rule, and also in reducing its storage and computational requirements. This work reviews some well-known editing algorithms for NN classification and presents alternative approaches based on combining the NN and the Nearest Centroid Neighbourhood of a sample. Finally, an empirical analysis over real data sets is provided.

This work has partially been supported by grants TIC2000-1703-C03-03 from the Spanish CICYT, SAB2001-0004 from the Spanish MECD, and 32016-A from the Mexican CONACyT.

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Author information

Authors and Affiliations

  1. Dept. Llenguatges i Sistemes Informàtics, U. Jaume I, 12071, Castelló, Spain

    J. S. Sánchez

  2. Av. Tecnológico s/n, Instituto Tecnológico de Toluca, 52140, Metepec, México

    R. Barandela

  3. Dept. d’Informàtica, U. València, 46100, Burjassot (Valéncia), Spain

    F. J. Ferri

Authors
  1. J. S. Sánchez
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  2. R. Barandela
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  3. F. J. Ferri
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Editor information

Editors and Affiliations

  1. Departament d’Enginyeria i Ciència dels Computadors, Universitat Jaume 1, Campus de Riu Sec, 12071, Castellón, Spain

    M. Teresa Escrig  & Francisco Toledo  & 

  2. Computer Science Department, Universitat Ramon Llull, Passeig Bonanova, 8, 08022, Barcelona, Catalunya, Spain

    Elisabet Golobardes

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© 2002 Springer-Verlag Berlin Heidelberg

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Sánchez, J.S., Barandela, R., Ferri, F.J. (2002). On Filtering the Training Prototypes in Nearest Neighbour Classification. In: Escrig, M.T., Toledo, F., Golobardes, E. (eds) Topics in Artificial Intelligence. CCIA 2002. Lecture Notes in Computer Science(), vol 2504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36079-4_21

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  • DOI: https://doi.org/10.1007/3-540-36079-4_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00011-2

  • Online ISBN: 978-3-540-36079-7

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