Learning possibilistic networks from data: a survey

DOI

10.2991/ifsa-eusflat-15.2015.30How to use a DOI?

Keywords

Possibility theory, graphical models, possibilistic networks, machine learning.

Abstract

Possibilistic networks are important tools for modelling and reasoning, especially in the presence of imprecise and/or uncertain information. These graphical models have been successfully used in several real applications. Since their construction by experts is complex and time consuming, several researchers have tried to learn them from data. In this paper, we try to present and discuss relevant state-of-the-art works related to learning possibilistic networks structure from data. In fact, we give an overview of methods that have already been proposed in this context and limitations of each one of them towards recent researches developed in possibility theory framework. We also present two learning possibilistic networks parameters methods.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Maroua Haddad
AU  - Philippe Leray
AU  - Nahla B. Amor
PY  - 2015/06
DA  - 2015/06
TI  - Learning possibilistic networks from data: a survey
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 194
EP  - 201
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.30
DO  - 10.2991/ifsa-eusflat-15.2015.30
ID  - Haddad2015/06
ER  -