Memorias de investigación
Artículos en revistas:
Tractable learning of Bayesian networks from partially observed data
Año:2019
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
The majority of real-world problems require addressing incomplete data. The use of the structural expectation-maximization algorithm is the most common approach toward learning Bayesian networks from incomplete datasets. However, its main limitation is its demanding computational cost, caused mainly by the need to make an inference at each iteration of the algorithm. In this paper, we propose a new method with the purpose of guaranteeing the efficiency of the learning process while improving the performance of the structural expectation-maximization algorithm. We address the first objective by applying an upper bound to the treewidth of the models to limit the complexity of the inference. To achieve this, we use an efficient heuristic to search the space of the elimination orders. For the second objective, we study the advantages of directly computing the score with respect to the observed data rather than an expectation of the score, and provide a strategy to efficiently perform these computations in the proposed method. We perform exhaustive experiments on synthetic and real-world datasets of varied dimensionalities, including datasets with thousands of variables and hundreds of thousands of instances. The experimental results support our claims empirically.
Internacional
Si
JCR del ISI
Si
Título de la revista
Pattern Recognition
ISSN
0031-3203
Factor de impacto JCR
3,965
Información de impacto
Posición 16 (Q1) en el área Computer Science, Artificial Intelligence de acuerdo a Thomson Reuters Journal Citation Reports
Volumen
91
DOI
10.1016/j.patcog.2019.02.025
Número de revista
Desde la página
190
Hasta la página
199
Mes
JULIO
Ranking
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Computational Intelligence Group
  • Departamento: Inteligencia Artificial