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Adapting Decision DAGs for Multipartite Ranking

dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorMontañés Roces, Elena 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorCoz Velasco, Juan José del
dc.descriptionEuropean Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010spa
dc.description.abstractMultipartite 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 classi cation 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 stateof- the-art methods, specially the one based on a probabilistic approach, called PR-DDAGspa
dc.description.sponsorshipThis research has been partially supported by Spanish Ministerio de Ciencia e Innovaci on (MICINN) grants TIN2007-61273 and TIN2008-06247 and by FICYT, Asturias, Spain, under grant IB09-059-C2spa
dc.format.extentp. 115-130spa
dc.relation.ispartofMachine Learning and Knowledge Discovery in Databasesspa
dc.rights© 2010 Springer
dc.titleAdapting Decision DAGs for Multipartite Rankingspa
dc.typebook partspa
dc.relation.projectIDPrincipado de Asturias-FICYT/IB09-059-C2
dc.rights.accessRightsopen access

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