Towards Gaussian Bayesian network fusion

Córdoba Sánchez, Irene; Bielza Lozoya, María Concepción y Larrañaga Múgica, Pedro María (2015). Towards Gaussian Bayesian network fusion. En: "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.

Descripción

Título: Towards Gaussian Bayesian network fusion
Autor/es:
  • Córdoba Sánchez, Irene
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Fechas del Evento: 15-17 Jul 2015
Lugar del Evento: Compiègne, Francia
Título del Libro: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Fecha: 2015
ISBN: 978-3-319-20806-0
Volumen: 9161
Materias:
Palabras Clave Informales: Gaussian Bayesian network; Fusion; Scalability; Big data
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Comunidad de MadridS2013/ICE-2845CASI - CAMSin especificarConceptos y Aplicaciones de los Sistemas Inteligentes
Gobierno de EspañaTIN2013-41592-PSin especificarUniversidad Politécnica de MadridSin especificar

Más información

ID de Registro: 41615
Identificador DC: http://oa.upm.es/41615/
Identificador OAI: oai:oa.upm.es:41615
Identificador DOI [BETA]: 10.1007/978-3-319-20807-7 47
URL Oficial: http://download.springer.com/static/pdf/615/chp%253A10.1007%252F978-3-319-20807-7_47.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-319-20807-7_47&token2=exp=1484065320~acl=%2Fstatic%2Fpdf%2F615%2Fchp%25253A10.1007%25252F978-3-31
Depositado por: Memoria Investigacion
Depositado el: 11 Ene 2017 08:58
Ultima Modificación: 11 Ene 2017 08:58
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