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Soft margin trees

Autor(es) y otros:
Díez Peláez, JorgeAutoridad Uniovi; Coz Velasco, Juan José delAutoridad Uniovi; Bahamonde Rionda, AntonioAutoridad Uniovi; Luaces Rodríguez, ÓscarAutoridad Uniovi
Fecha de publicación:
2009
Editorial:

Springer

Versión del editor:
http://dx.doi.org/10.1007/978-3-642-04180-8_37
Descripción física:
p. 302-314
Resumen:

From a multi-class learning task, in addition to a classi er, it is possible to infer some useful knowledge about the relationship between the classes involved. In this paper we propose a method to learn a hierarchical clustering of the set of classes. The usefulness of such clusterings has been exploited in bio-medical applications to nd out relations between diseases or populations of animals. The method proposed here de nes a distance between classes based on the margin maximization principle, and then builds the hierarchy using a linkage procedure. Moreover, to quantify the goodness of the hierarchies we de ne a measure. Finally, we present a set of experiments comparing the scores achieved by our approach with other methods

From a multi-class learning task, in addition to a classi er, it is possible to infer some useful knowledge about the relationship between the classes involved. In this paper we propose a method to learn a hierarchical clustering of the set of classes. The usefulness of such clusterings has been exploited in bio-medical applications to nd out relations between diseases or populations of animals. The method proposed here de nes a distance between classes based on the margin maximization principle, and then builds the hierarchy using a linkage procedure. Moreover, to quantify the goodness of the hierarchies we de ne a measure. Finally, we present a set of experiments comparing the scores achieved by our approach with other methods

Descripción:

European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009; Bled; 7 September 2009 through 11 September 2009

URI:
http://hdl.handle.net/10651/12379
ISBN:
978-3-642-04179-2; 978-3-642-04180-8
Identificador local:

20090165

DOI:
10.1007/978-3-642-04180-8_37
Patrocinado por:

The research reported here is supported in part under grant TIN2008-06247 from the MICINN (Ministerio de Ciencia e Innovación of Spain)

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