Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42884
Title: Detection of interdependences in attribute selection
Authors: Lorenzo, Javier 
Hernández, Mario 
Mendez, J 
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 1998
Journal: Lecture Notes in Computer Science 
Abstract: A new measure for attribute selection, called GD, is proposed. The GD measure is based on Information Theory and allows to detect the interdependence between attributes. This measure is based on a quadratic form of the Mántaras distance and a matrix called Transinformation Matrix. In order to test the quality of the proposed measure, it is compared with other two feature selection methods, namely Mántaras distance and Relief algorithms. The comparison is done over 19 datasets along with three different induction algorithms.
URI: http://hdl.handle.net/10553/42884
ISBN: 978-3-540-65068-3
ISSN: 0302-9743
DOI: 10.1007/BFb0094822
Source: Żytkow J.M., Quafafou M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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