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Adapting Decision DAGs for Multipartite Ranking

  • José Ramón Quevedo
  • Elena Montañés
  • Oscar Luaces
  • Juan José del Coz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6323)

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.

Keywords

Base Learner Ordinal Regression Competent Model Global Ranking Machine Learn Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • José Ramón Quevedo
    • 1
  • Elena Montañés
    • 1
  • Oscar Luaces
    • 1
  • Juan José del Coz
    • 1
  1. 1.Artificial Intelligence CenterUniversity of Oviedo at GijónSpain

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