Tractable learning of Bayesian networks from partially observed data

Benjumeda Barquita, Marco Alberto, Luengo Sánchez, Sergio ORCID:, Larrañaga Múgica, Pedro María ORCID: and Bielza Lozoya, María Concepción ORCID: (2019). Tractable learning of Bayesian networks from partially observed data. "Pattern Recognition", v. 91 ; pp. 190-199. ISSN 0031-3203.


Title: Tractable learning of Bayesian networks from partially observed data
Item Type: Article
Título de Revista/Publicación: Pattern Recognition
Date: July 2019
ISSN: 0031-3203
Volume: 91
Freetext Keywords: Structural expectation-maximization; Bayesian network; Incomplete data; Inference complexity; Structure learning
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

Government of Spain
The Cajal Blue Brain
Government of Spain
Universidad Politécnica de Madrid
Avances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional Government
Conceptos y aplicaciones de los sistemas inteligentes
Horizon 2020
École Polytechnique Fédérale de Lausanne
Human Brain Project Specific Grant Agreement 2
Government of Spain
Universidad Politécnica de Madrid
Predoctoral contract for the formation of doctors

More information

Item ID: 63470
DC Identifier:
OAI Identifier:
DOI: 10.1016/j.patcog.2019.02.025
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 27 Oct 2020 07:59
Last Modified: 30 Nov 2022 09:00
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