Memorias de investigación
Research Publications in journals:
Tractable learning of Bayesian networks from partially observed data
Year:2019
Research Areas
  • Information technology and adata processing
Information
Abstract
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.
International
Si
JCR
Si
Title
Pattern Recognition
ISBN
0031-3203
Impact factor JCR
3,965
Impact info
Posición 16 (Q1) en el área Computer Science, Artificial Intelligence de acuerdo a Thomson Reuters Journal Citation Reports
Volume
91
10.1016/j.patcog.2019.02.025
Journal number
From page
190
To page
199
Month
JULIO
Ranking
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Computational Intelligence Group
  • Departamento: Inteligencia Artificial