%0 Journal Article
%1 jorge2009margin
%A Díez, Jorge
%A del Coz, Juan
%A Bahamonde, Antonio
%A Luaces, Oscar
%D 2009
%J Machine Learning and Knowledge Discovery in Databases
%K 2009 classes clustering ecml metric pkdd
%P 302--314
%T Soft Margin Trees
%U http://dx.doi.org/10.1007/978-3-642-04180-8_37
%X From a multi-class learning task, in addition to a classifier, 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 find out relations between diseases orpopulations of animals. The method proposed here defines 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 define ameasure. Finally, we present a set of experiments comparing the scores achieved by our approach with other methods.