A BAYESIAN NETWORKS STRUCTURAL LEARNING ALGORITHM BASED ON A MULTIEXPERT APPROACH

Francesco Colace, Massimo De Santo, Mario Vento, Pasquale Foggia

2005

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 performance both in terms of the network topological reconstruction and of the correct orientation of the obtained arcs.

Paper Citation

in Harvard Style

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, pages 194-200. DOI: 10.5220/0002521401940200

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Colace F.
AU - De Santo M.
AU - Vento M.
AU - Foggia P.
PY - 2005
SP - 194
EP - 200
DO - 10.5220/0002521401940200