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European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2015: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 312-321 | Cite as

Evaluating Product-Based Possibilistic Networks Learning Algorithms

  • Maroua HaddadEmail author
  • Philippe Leray
  • Nahla Ben Amor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9161)

Abstract

This paper proposes a new evaluation strategy for product-based possibilistic networks learning algorithms. The proposed strategy is mainly based on sampling a possibilistic networks in order to construct an imprecise data set representative of their underlying joint distribution. Experimental results showing the efficiency of the proposed method in comparing existing possibilistic networks learning algorithms is also presented.

Keywords

Bayesian Network Possibility Distribution Possibility Theory Graphical Component Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maroua Haddad
    • 1
    • 2
    Email author
  • Philippe Leray
    • 2
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC Laboratory ISGUniversité de TunisTunisTunisia
  2. 2.LINA-UMR CNRS 6241University of NantesNantesFrance

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