loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Imen Ben Brahim 1 ; Sid-Ali Addouche 2 ; Abderrahman El Mhamedi 2 and Younes Boujelbene 3

Affiliations: 1 QUARTZ Laboratory EA 7393, IUT of Montreuil, University of Paris 8, 140, rue de la Nouvelle-France, 93100 Montreuil, France, URECA, University of Sfax, Airport Road Km4, 3018 Sfax and Tunisia ; 2 QUARTZ Laboratory EA 7393, IUT of Montreuil, University of Paris 8, 140, rue de la Nouvelle-France, 93100 Montreuil and France ; 3 URECA, University of Sfax, Airport Road Km4, 3018 Sfax and Tunisia

Keyword(s): Elicitation, Bayesian Network, Weighted Sum Algorithm, Clustering, Decision Support.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Knowledge-Based Systems ; Symbolic Systems

Abstract: A knowledge representation and reasoning from data have produced many models. Probabilistic graphical models, specifically the Bayesian Network (BN) have proved its worth. It is considered to be a very useful tool for representing uncertain knowledge and decision-making support. This presupposes availability of knowledge problem in the conditional probabilities form. However, one is often in a critical situation because of data are insufficient, partially unavailable or heterogeneous. Developing methods and techniques to reconstruct the corpus of data needed for decision making, especially via BN is called the ”knowledge elicitation”. Several elicitation methods exist but they are not always applicable, too demanding in expert knowledge or presenting limits. The most generic and useful is the Weighted Sum Algorithm (WSA) but it presents two major issues concerning the compatible parental configuration. In the present paper, we discuss what the literature proposes for the first one, t hen we develop the solution for the second and validate it via a case of pump failure prognostic tool based on Bayesian support decision. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.79.169

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ben Brahim, I.; Addouche, S.; El Mhamedi, A. and Boujelbene, Y. (2018). Cluster-Based WSA for Decision Support Bayesian Systems: Case of Prognostic in Maintenance Management. In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-321-6; ISSN 2184-2809, SciTePress, pages 222-230. DOI: 10.5220/0006878502220230

@conference{icinco18,
author={Imen {Ben Brahim}. and Sid{-}Ali Addouche. and Abderrahman {El Mhamedi}. and Younes Boujelbene.},
title={Cluster-Based WSA for Decision Support Bayesian Systems: Case of Prognostic in Maintenance Management},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2018},
pages={222-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006878502220230},
isbn={978-989-758-321-6},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Cluster-Based WSA for Decision Support Bayesian Systems: Case of Prognostic in Maintenance Management
SN - 978-989-758-321-6
IS - 2184-2809
AU - Ben Brahim, I.
AU - Addouche, S.
AU - El Mhamedi, A.
AU - Boujelbene, Y.
PY - 2018
SP - 222
EP - 230
DO - 10.5220/0006878502220230
PB - SciTePress