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Communication Dans Un Congrès Année : 2016

Possibilistic networks parameter learning: Preliminary empirical comparison

Résumé

Like Bayesian networks, possibilistic ones compactly encode joint uncertainty representations over a set of variables. Learning possibilistic networks from data in general and from imperfect or scarce data in particular, has not received enough attention. Indeed, only few works deal with learning the structure and the parameters of a possibilistic network from a dataset. This paper provides a preliminary comparative empirical evaluation of two approaches for learning the parameters of a possibilistic network from empirical data. The first method is a possibilistic approach while the second one first learns imprecise probability measures then transforms them into possibility distributions by means of probability-possibility transformations. The comparative evaluation focuses on learning belief networks on datasets with missing data and scarce datasets.
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Dates et versions

hal-01347810 , version 1 (21-07-2016)

Identifiants

  • HAL Id : hal-01347810 , version 1

Citer

Maroua Haddad, Philippe Leray, Amélie Levray, Karim Tabia. Possibilistic networks parameter learning: Preliminary empirical comparison. 8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France. pp.?-?. ⟨hal-01347810⟩
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