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Memorias de investigación
Ponencias en congresos:
Learning CB-decomposable Multi-dimensional Bayesian Network Classifiers
Año:2010
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Multi-dimensional Bayesian network classifiers (MBCs) have been recently introduced to deal with multi-dimensional classification problems where instances are assigned to multiple classes. MBCs have a restricted topology partitioning the set of class and feature variables into three different subgraphs: class subgraph, feature subgraph and bridge subgraph. In this paper, we propose a novel learning algorithm for class-bridge (CB) decomposable MBCs into maximal connected components. Basically, based on a wrapper greedy forward selection approach, the algorithm firstly learns the bridge and feature subgraphs. Then, while the number of components is greater than one and there is an accuracy improvement, it iteratively and sequentially merges together the components, and updates the bridge and feature subgraphs. By learning CB-decomposable MCBs, the computations of MPE are alleviated comparing to general MBCs. Experimental comparison with state-of-the-art algorithms are carried out using synthetic and real-world data sets. The obtained results show the merits of our proposed algorithm.
Internacional
Si
Nombre congreso
5th European Workshop on Probabilistic Graphical Models (PGM2010)
Tipo de participación
960
Lugar del congreso
Helsinki, Finlandia
Revisores
Si
ISBN o ISSN
1458-946X
DOI
Fecha inicio congreso
13/09/2010
Fecha fin congreso
15/09/2010
Desde la página
25
Hasta la página
33
Título de las actas
Proceedings of the Fifth European Workshop on Probabilistic Graphical Models
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Hanen Borchani (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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