Adapting Decision DAGs for Multipartite Ranking
Abstract
Multipartite ranking is a special kind of ranking for problems in which classes exhibit an order. Many applications require its use, for instance, granting loans in a bank, reviewing papers in a conference or just grading exercises in an education environment. Several methods have been proposed for this purpose. The simplest ones resort to regression schemes with a pre- and post-process of the classes, what makes them barely useful. Other alternatives make use of class order information or they perform a pairwise classification together with an aggregation function. In this paper we present and discuss two methods based on building a Decision Directed Acyclic Graph (DDAG). Their performance is evaluated over a set of ordinal benchmark data sets according to the C-Index measure. Both yield competitive results with regard to state-of-the-art methods, specially the one based on a probabilistic approach, called PR-DDAG.
Cite
Text
Quevedo et al. "Adapting Decision DAGs for Multipartite Ranking." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_8Markdown
[Quevedo et al. "Adapting Decision DAGs for Multipartite Ranking." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/quevedo2010ecmlpkdd-adapting/) doi:10.1007/978-3-642-15939-8_8BibTeX
@inproceedings{quevedo2010ecmlpkdd-adapting,
title = {{Adapting Decision DAGs for Multipartite Ranking}},
author = {Quevedo, José Ramón and Montañés, Elena and Luaces, Oscar and del Coz, Juan José},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {115-130},
doi = {10.1007/978-3-642-15939-8_8},
url = {https://mlanthology.org/ecmlpkdd/2010/quevedo2010ecmlpkdd-adapting/}
}