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http://hdl.handle.net/10553/42884
| Título: | Detection of interdependences in attribute selection | Autores/as: | Lorenzo, Javier Hernández, Mario Mendez, J |
Clasificación UNESCO: | 120304 Inteligencia artificial | Palabras clave: | Decision Tree Induction | Fecha de publicación: | 1998 | Publicación seriada: | Lecture Notes in Computer Science | Conferencia: | 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 |
Resumen: | 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 | Fuente: | 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 |
| Colección: | Actas de congresos |
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