%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.

@article{jorge2009margin, 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 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.}, added-at = {2009-09-10T14:05:17.000+0200}, author = {Díez, Jorge and del Coz, Juan and Bahamonde, Antonio and Luaces, Oscar}, biburl = {https://www.bibsonomy.org/bibtex/2fa3f2f8d6a9103c72fd8a32ca0d1e247/folke}, description = {SpringerLink - Book Chapter}, interhash = {634c6107fd84fc2f0b17bf1559436a89}, intrahash = {fa3f2f8d6a9103c72fd8a32ca0d1e247}, journal = {Machine Learning and Knowledge Discovery in Databases}, keywords = {2009 classes clustering ecml metric pkdd}, pages = {302--314}, timestamp = {2009-09-10T14:07:56.000+0200}, title = {Soft Margin Trees}, url = {http://dx.doi.org/10.1007/978-3-642-04180-8_37}, year = 2009 }