Soft Margin Trees
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.
Cite
Text
Díez et al. "Soft Margin Trees." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_37Markdown
[Díez et al. "Soft Margin Trees." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/diez2009ecmlpkdd-soft/) doi:10.1007/978-3-642-04180-8_37BibTeX
@inproceedings{diez2009ecmlpkdd-soft,
title = {{Soft Margin Trees}},
author = {Díez, Jorge and del Coz, Juan José and Bahamonde, Antonio and Luaces, Oscar},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {302-314},
doi = {10.1007/978-3-642-04180-8_37},
url = {https://mlanthology.org/ecmlpkdd/2009/diez2009ecmlpkdd-soft/}
}