Detection of interdependences in attribute selection

Title:	Detection of interdependences in attribute selection
Authors:	Lorenzo, Javier Hernández, Mario Mendez, J
UNESCO Clasification:	120304 Inteligencia artificial
Keywords:	Decision Tree Induction
Issue Date:	1998
Journal:	Lecture Notes in Computer Science
Conference:	2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases (PKDD 98) 2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998
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 3540650687
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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