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Communication Dans Un Congrès Année : 2017

Learning the parameters of possibilistic networks from data: Empirical comparison

Résumé

Possibilistic networks are belief graphical models based on possibility theory. A possibilistic network either represents experts' epistemic uncertainty or models uncertain information from poor, scarce or imprecise data. Learning possibilistic networks from data in general and from imperfect or scarce datasets in particular, has not received enough attention. This work focuses on parameter learning of possibilistic networks. The main contributions of the paper are i) a study of an extension of the information affinity measure to assess the similarity of possibilistic networks and ii) a comparative empirical evaluation of two approaches for learning the parameters of a possibilistic network from empirical data.
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Dates et versions

hal-01532494 , version 1 (15-04-2020)

Identifiants

  • HAL Id : hal-01532494 , version 1

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Maroua Haddad, Philippe Leray, Amélie Levray, Karim Tabia. Learning the parameters of possibilistic networks from data: Empirical comparison. Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 30), 2017, Marco Island, United States. ⟨hal-01532494⟩
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