Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/42882
Título: GD: a measure based on information theory for attribute selection
Autores/as: Lorenzo, Javier 
Hernández, Mario 
Mendez, Juan 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Machine learning
Intelligent information retrieval
Feature selection
Fecha de publicación: 1998
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 6th Ibero-American Congress on Artificial Intelligence (IBERAMIA 98) 
6th Ibero-American Congress on Artificial Intelligence, IBERAMIA 1998 
Resumen: 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.
URI: http://hdl.handle.net/10553/42882
ISBN: 978-3-540-64992-2
3540649921
ISSN: 0302-9743
DOI: 10.1007/3-540-49795-1_11
Fuente: Coelho H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science, vol 1484. Springer, Berlin, Heidelberg
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