Learning the parameters of possibilistic networks from data: Empirical comparison

Maroua Haddad, Philippe Leray, Amelie Levray, Karim Tabia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication30th International FLAIRS Conference (FLAIRS’17), 2017
Place of PublicationMarco Island, Florida, USA
PublisherAAAI Press
Number of pages6
ISBN (Print)978-1-57735-787-2
Publication statusPublished - 8 May 2017
Event30th International FLAIRS Conference 2017 - Marco Island, United States
Duration: 22 May 201724 May 2017


Conference30th International FLAIRS Conference 2017
Abbreviated titleFLAIRS’17
Country/TerritoryUnited States
CityMarco Island


Dive into the research topics of 'Learning the parameters of possibilistic networks from data: Empirical comparison'. Together they form a unique fingerprint.

Cite this