Paper Info
Title
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15939-8_8
ECML/PKDD (3)
Keywords
Field
DocType
aggregation function,multipartite ranking,decision directed acyclic graph,c-index measure,ordinal benchmark data,grading exercise,education environment,class order information,pairwise classification,adapting decision dags,probabilistic approach,indexation
Pairwise comparison,Multipartite,Regression,Ranking,Computer science,Ordinal number,Directed acyclic graph,Ordinal regression,Artificial intelligence,Probabilistic logic,Machine learning
Conference
Volume
ISSN
ISBN
6323
0302-9743
3-642-15938-9
Citations 
PageRank 
References 
3
0.37
23
Authors
4
Name
Order
Citations
PageRank
José Ramón Quevedo117515.37
Elena Montanes216815.24
Oscar Luaces328124.59
Juan José Del Coz431222.86