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Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2143))

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Abstract

An important type of methods for learning belief networks from data are those based on the use of a scoring metric, to evaluate the fitness of any given candidate network to the data base, and a search procedure to explore the set of candidate networks. In this paper we propose a new method that carries out the search not in the space of directed acyclic graphs but in the space of the orderings of the variables that compose the graphs. Moreover, we use a new stochastic search method to be applied to this problem, Variable Neighborhood Search. We also experimentally compare our methods with some other search procedures commonly used in the literature.

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Author information

Authors and Affiliations

  1. Dpto de Ciencias de la Computación e I.A, Universidad de Granada, 18071, Granada, Spain

    Luis M. de Campos

  2. Dpto de Informática, Universidad de Castilla-La Mancha, 02071, Albacete, Spain

    J. Miguel Puerta

Authors
  1. Luis M. de Campos
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  2. J. Miguel Puerta
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Editor information

Editors and Affiliations

  1. Université Paul Sabatier, IRIT-CNRS, 118 route de Narbonne, 31062, Toulouse Cedex 4, France

    Salem Benferhat  & Philippe Besnard  & 

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de Campos, L.M., Puerta, J.M. (2001). Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings. In: Benferhat, S., Besnard, P. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2001. Lecture Notes in Computer Science(), vol 2143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44652-4_21

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  • DOI: https://doi.org/10.1007/3-540-44652-4_21

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  • Print ISBN: 978-3-540-42464-2

  • Online ISBN: 978-3-540-44652-1

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