On the equivalency between decision tree classifiers and the nearest neighbour rule

  1. J.S. Sánchez 1
  2. F. Pla 1
  3. F.J. Ferri 2
  1. 1 Universitat Jaume I
    info

    Universitat Jaume I

    Castelló de la Plana, España

    ROR https://ror.org/02ws1xc11

  2. 2 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

Libro:
CAEPIA'97: actas
  1. Botti, Vicent (coord.)

Editorial: Vicent Botti ; Asociación Española para la Inteligencia Artificial (AEPIA)

ISBN: 978-84-8498-765-9,  84-8498-765-5

Año de publicación: 1997

Páginas: 197-206

Congreso: Conferencia de la Asociación Española para la Inteligencia Artificial. (7. 1997. null)

Tipo: Aportación congreso

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Resumen

This paper introduces a method for designing a binary tree structured classifier by using the decision boundaries associated to a reduced set of prototypes. The purpose of this technique consists of finding a classification scheme whose result will be equivalent to that produced by the Nearest Neighbour rule, but with the important advantage of being much faster in terms of classification time. Experiments on real and artificial data sets demonstrate that the design procedure consistently finds decision trees with that equivalency property.

Fuente de los datos: Dialnet