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Towards Gaussian Bayesian Network Fusion

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9161))

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.

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Acknowledgements

The authors thank the reviewers for comments and critics which significantly contributed to improve the paper; and also J.M. Peña, J. Nielsen, J. Mengin and M. Serrurier for the valuable help. This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through the Cajal Blue Brain (C080020-09; the Spanish partner of the Blue Brain initiative from EPFL) and TIN2013-41592-P projects, and by the Regional Government of Madrid through the S2013/ICE-2845-CASI-CAM-CM project.

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Authors and Affiliations

  1. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Irene Córdoba-Sánchez, Concha Bielza & Pedro Larrañaga

Authors
  1. Irene Córdoba-Sánchez
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  2. Concha Bielza
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  3. Pedro Larrañaga
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Corresponding author

Correspondence to Irene Córdoba-Sánchez .

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Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Sébastien Destercke

  2. Centre de Recherches de Royallieu, Université de Technologie de Compiègne, Compiègne, France

    Thierry Denoeux

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Córdoba-Sánchez, I., Bielza, C., Larrañaga, P. (2015). Towards Gaussian Bayesian Network Fusion. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_47

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  • DOI: https://doi.org/10.1007/978-3-319-20807-7_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20806-0

  • Online ISBN: 978-3-319-20807-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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