Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy

Maroua Haddad (1, 2) , Philippe Leray (1) , Nahla Ben Amor (2)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus

There has been an ever-increasing interest in multidisciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the rst part of this paper a possibilistic networks sampling process. In the second part, we propose a likelihood function which explores the link between random sets theory and possibility theory. This function is then deployed to parametrize possibilistic networks.

Domaines: Apprentissage [cs.LG]

Maroua Haddad, Philippe Leray, Nahla Ben Amor. Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy. [Research Report] Laboratoire d'Informatique de Nantes Atlantique. 2016. ⟨hal-01344821⟩