Jorge Díez, Artificial Inteligence Centre - University of Oviedo at Gijón, Spain
Juan José del Coz, Artificial Inteligence Centre - University of Oviedo at Gijón, Spain
Antonio Bahamonde, Artificial Inteligence Centre - University of Oviedo at Gijón, Spain
Oscar Luaces, Artificial Intelligence Centre - University of Oviedo at Gijón, Spain

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Abstract

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 or populations 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 a measure. Finally, we present a set of experiments comparing the scores achieved by our approach with other methods