Soft margin trees
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Springer
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Abstract:
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
Description:
European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009; Bled; 7 September 2009 through 11 September 2009
ISBN:
Identificador local:
20090165
Patrocinado por:
The research reported here is supported in part under grant TIN2008-06247 from the MICINN (Ministerio de Ciencia e Innovación of Spain)
Id. Proyecto:
MICIIN/TIN2008-06247
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