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Fast k Most Similar Neighbor Classifier for Mixed Data Based on a Tree Structure

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

Abstract

In this work, a fast k most similar neighbor (k-MSN) classifier for mixed data is presented. The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition. Many fast k-NN classifiers have been developed to be applied on numerical object descriptions, most of them based on metric properties to avoid object comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical features (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, our classifier is based on search algorithms suitable for mixed data and non-metric comparison functions. Some experiments and a comparison against other two fast k-NN methods, using standard databases, are presented.

Keywords

Nearest Neighbors Rule Fast k-Most Similar Neighbors Search Mixed Data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Selene Hernández-Rodríguez
    • 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: 72840México

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