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Authors: Francesco Colace 1 ; Massimo De Santo 1 ; Mario Vento 1 and Pasquale Foggia 2

Affiliations: 1 DIIIE, Università degli Studi di Salerno, Italy ; 2 DIS, Università di Napoli “Federico II”, Italy

Keyword(s): Bayesian Networks, MultiExpert System

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Biomedical Engineering ; Data Engineering ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Management ; Ontologies and the Semantic Web ; Society, e-Business and e-Government ; Soft Computing ; Web Information Systems and Technologies

Abstract: The determination of Bayesian network structure, especially in the case of large domains, can be complex, time consuming and imprecise. Therefore, in the last years, the interest of the scientific community in learning Bayesian network structure from data is increasing. This interest is motivated by the fact that many techniques or disciplines, as data mining, text categorization, ontology building, can take advantage from structural learning. In literature we can find many structural learning algorithms but none of them provides good results in every case or dataset. In this paper we introduce a method for structural learning of Bayesian networks based on a multiexpert approach. Our method combines the outputs of five structural learning algorithms according to a majority vote combining rule. The combined approach shows a performance that is better than any single algorithm. We present an experimental validation of our algorithm on a set of “de facto” standard networks, measuring pe rformance both in terms of the network topological reconstruction and of the correct orientation of the obtained arcs. (More)

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Paper citation in several formats:
Colace, F.; De Santo, M.; Vento, M. and Foggia, P. (2005). A BAYESIAN NETWORKS STRUCTURAL LEARNING ALGORITHM BASED ON A MULTIEXPERT APPROACH. In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-19-8; ISSN 2184-4992, SciTePress, pages 194-200. DOI: 10.5220/0002521401940200

@conference{iceis05,
author={Francesco Colace. and Massimo {De Santo}. and Mario Vento. and Pasquale Foggia.},
title={A BAYESIAN NETWORKS STRUCTURAL LEARNING ALGORITHM BASED ON A MULTIEXPERT APPROACH},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2005},
pages={194-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002521401940200},
isbn={972-8865-19-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - A BAYESIAN NETWORKS STRUCTURAL LEARNING ALGORITHM BASED ON A MULTIEXPERT APPROACH
SN - 972-8865-19-8
IS - 2184-4992
AU - Colace, F.
AU - De Santo, M.
AU - Vento, M.
AU - Foggia, P.
PY - 2005
SP - 194
EP - 200
DO - 10.5220/0002521401940200
PB - SciTePress