EFFICIENT LEARNING OF DYNAMIC BAYESIAN NETWORKS FROM TIMED DATA

Ahmad Ahdab, Marc Le Goc

2010

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.

References

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Paper Citation


in Harvard Style

Ahdab A. and Le Goc M. (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


in Bibtex Style

@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},
}


in EndNote Style

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