Detection of interdependences in attribute selection

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Article 01 Jan 1998Publisher:Springer Berlin Heidelberg

Authors: Javier Lorenzo 0001; Mario Hernández; Juan Méndez;

doi: 10.1007/bfb0094822

handle: 10553/42884

Detection of interdependences in attribute selection

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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.

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Related Organizations

University of Las Palmas de Gran Canaria
Spain

Keywords

120304 Inteligencia artificial, Decision Tree Induction

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