Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Learning possibilistic networks from data: a survey

Authors
Maroua Haddad, Philippe Leray, Nahla B. Amor
Corresponding Author
Maroua Haddad
Available Online June 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

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  - 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.30
DO  - https://doi.org/10.2991/ifsa-eusflat-15.2015.30
ID  - Haddad2015/06
ER  -