European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2015: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 519-528 | Cite as

Towards Gaussian Bayesian Network Fusion

  • Irene Córdoba-Sánchez
  • Concha Bielza
  • Pedro Larrañaga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Gaussian Bayesian network Fusion Scalability Big data 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Irene Córdoba-Sánchez
    • 1
  • Concha Bielza
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridMadridSpain

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