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Authors: Ahmad Ahdab and Marc Le Goc

Affiliation: Université Paul Cézanne, France

Keyword(s): Machine Learning, Bayesian Network, Stochastic Representation, Data Mining, Knowledge Discovery.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Acquisition ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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 a new algorithm to learn the structure of a Dynamic Bayesian Network and to orient the edges from the timed data contained in a given timed data base. This algorithm is 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 a theoretical example before presenting the results on an application on the Apache system of the Arcelor-Mittal Steel Group, a real world knowledge based system that diagn oses a galvanization bat (More)

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Paper citation in several formats:
Ahdab, A. and Le Goc, M. (2010). LEARNING DYNAMIC BAYESIAN NETWORKS WITH THE TOM4L PROCESS. In Proceedings of the 5th International Conference on Software and Data Technologies - Volume 2: ICSOFT; ISBN 978-989-8425-23-2; ISSN 2184-2833, SciTePress, pages 353-363. DOI: 10.5220/0002928603530363

@conference{icsoft10,
author={Ahmad Ahdab. and Marc {Le Goc}.},
title={LEARNING DYNAMIC BAYESIAN NETWORKS WITH THE TOM4L PROCESS},
booktitle={Proceedings of the 5th International Conference on Software and Data Technologies - Volume 2: ICSOFT},
year={2010},
pages={353-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002928603530363},
isbn={978-989-8425-23-2},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Software and Data Technologies - Volume 2: ICSOFT
TI - LEARNING DYNAMIC BAYESIAN NETWORKS WITH THE TOM4L PROCESS
SN - 978-989-8425-23-2
IS - 2184-2833
AU - Ahdab, A.
AU - Le Goc, M.
PY - 2010
SP - 353
EP - 363
DO - 10.5220/0002928603530363
PB - SciTePress

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