Ibero-American Conference on Artificial Intelligence

IBERAMIA 1998: Progress in Artificial Intelligence — IBERAMIA 98 pp 124-135

GD: A Measure Based on Information Theory for Attribute Selection

  • Javier Lorenzo
  • Mario Hernández
  • Juan Méndez
Conference paper

DOI: 10.1007/3-540-49795-1_11

Volume 1484 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Lorenzo J., Hernández M., Méndez J. (1998) GD: A Measure Based on Information Theory for Attribute Selection. In: Coelho H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science, vol 1484. Springer, Berlin, Heidelberg

Abstract

In this work a measure called GD is presented for attribute selection. This measure is defined between an attribute set and a class and corresponds to a generalization of the Mántaras distance that allows to detect the interdependencies between attributes. In the same way, the proposed measure allows to order the attributes by importance in the definition of the concept. This measure does not exhibit a noticeable bias in favor of attributes with many values. The quality of the selected attributes using the GD measure is tested by means of different comparisons with other two attribute selection methods over 19 datasets.

Keywords

Machine learning Intelligent information retrieval Feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Javier Lorenzo
    • 1
  • Mario Hernández
    • 1
  • Juan Méndez
    • 1
  1. 1.Dpto. de Informática y SistemasUniv. de Las Palmas de Gran CanariaLas PalmasSpain