Towards Gaussian Bayesian network fusion

Córdoba Sánchez, Irene and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2015). Towards Gaussian Bayesian network fusion. In: "European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty", 15-17 Jul 2015, Compiègne, Francia. ISBN 978-3-319-20806-0. pp. 519-528. https://doi.org/10.1007/978-3-319-20807-7 47.

Description

Title: Towards Gaussian Bayesian network fusion
Author/s:
  • Córdoba Sánchez, Irene
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Presentation at Congress or Conference (Article)
Event Title: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Event Dates: 15-17 Jul 2015
Event Location: Compiègne, Francia
Title of Book: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Date: 2015
ISBN: 978-3-319-20806-0
Volume: 9161
Subjects:
Freetext Keywords: Gaussian Bayesian network; Fusion; Scalability; Big data
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are leading to the proposal of new methods and paradigms, in order to be able to deal with what is nowadays referred to as Big Data. In this paper we propose a method for the aggregation of different Bayesian network structures that have been learned from separate data sets, as a first step towards mining data sets that need to be partitioned in an horizontal way, i.e. with respect to the instances, in order to be processed. Considerations that should be taken into account when dealing with this situation are discussed. Scalable learning of Bayesian networks is slowly emerging, and our method constitutes one of the first insights into Gaussian Bayesian network aggregation from different sources. Tested on synthetic data it obtains good results that surpass those from individual learning. Future research will be focused on expanding the method and testing more diverse data sets.

Funding Projects

TypeCodeAcronymLeaderTitle
Madrid Regional GovernmentS2013/ICE-2845CASI - CAMUnspecifiedConceptos y Aplicaciones de los Sistemas Inteligentes
Government of SpainTIN2013-41592-PUnspecifiedUniversidad Politécnica de MadridUnspecified
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