Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42882
Title: GD: A measure based on information theory for attribute selection
Authors: Lorenzo, Javier 
Hernández, Mario 
Mendez, J 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Machine learning
Intelligent information retrieval
Feature selection
Issue Date: 1998
Journal: Lecture Notes in Computer Science 
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.
URI: http://hdl.handle.net/10553/42882
ISBN: 978-3-540-64992-2
ISSN: 0302-9743
DOI: 10.1007/3-540-49795-1_11
Source: Coelho H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science, vol 1484. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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