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Authors: Ahmad Ahdab and Marc Le Goc

Affiliation: LSIS, UMR CNRS 6168, Université Paul Cézanne, France

ISBN: 978-989-8425-05-8

Keyword(s): Machine Learning, Bayesian network, Stochastic representation, Data mining, Knowledge discovery.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Industrial Applications of Artificial Intelligence ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: This paper addresses the problem of learning a Dynamic Bayesian network from timed data without prior knowledge to the system. One of the main problems of learning a Dynamic Bayesian network is building and orienting the edges of the network avoiding loops. The problem is more difficult when data are timed. This paper proposes an algorithm based on an adequate representation of a set of sequences of timed data and uses an information based measure of the relations between two edges. This algorithm is a part of the Timed Observation Mining for Learning (TOM4L) process that is based on the Theory of the Timed Observations. The paper illustrates the algorithm with an application on the Apache system of the Arcelor-Mittal Steel Group, a real world knowledge based system that diagnoses a galvanization bath.

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Paper citation in several formats:
Ahdab A.; Le Goc M. and (2010). EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA.In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 226-231. DOI: 10.5220/0002897802260231

@conference{iceis10,
author={Ahmad Ahdab and Marc {Le Goc}},
title={EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={226-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002897802260231},
isbn={978-989-8425-05-8},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA
SN - 978-989-8425-05-8
AU - Ahdab, A.
AU - Le Goc, M.
PY - 2010
SP - 226
EP - 231
DO - 10.5220/0002897802260231

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