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Repositorio de la Universidad de Oviedo > Producción Bibliográfica de UniOvi: RECOPILA > Capítulos de libros >

Use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10651/35744

Título : Adapting Decision DAGs for Multipartite Ranking
Autor(es) y otros: Quevedo Pérez, José Ramón
Montañés Roces, Elena
Luaces Rodríguez, Óscar
Coz Velasco, Juan José del
Fecha de publicación : 2010
Editorial : Springer
Versión del editor: http://dx.doi.org/10.1007/978-3-642-15939-8_8
Descripción física: p. 115-130
Resumen : 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 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-DDAG
Descripción : European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010
URI : http://hdl.handle.net/10651/35744
ISBN : 978-3-642-15938-1
978-3-642-15939-8
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