BAYESIAN NETWORK STRUCTURAL LEARNING FROM DATA: AN ALGORITHMS COMPARISON

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

Abstract

The manual determination of Bayesian Network structure or, more in general, of the probabilistic models, in particular in the case of remarkable dimensions 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 considerably increased. In fact, many techniques or disciplines, as data mining, text categorization, ontology description, can take advantages from this type of processes. In this paper we will describe some possible approaches to the structural learning of bayesian networks and introduce in detail some algorithms deriving from these ones. We will aim to compare results obtained using the main algorithms on databases normally used in literature. With this aim, we have selected and implemented five algorithms more used in literature. We will estimate the algorithms performances both considering the network topological reconstruction both the correct orientation of the obtained arcs.

References

  1. Singh, M., Valtorta, M., 1995. Construction of Bayesian Network Structures from Data: a Brief Survey and an Efficient Algorithm. In International Journal of Approximate Reasoning
  2. Fung R. M., Crawford S. L., 1990. Constructor: a System For The Induction of Probabilistic Models. In Proceedings of AAAI-90
  3. Pearl J., Verma T., 1991. A Theory of Inferred Causation, In Principles of Knowledge Representation and Reasoning, Morgan Kaufmann
  4. Cooper G. F., E. Herskovits, 1992. A Bayesian Method For The Induction of Probabilistic Networks From Data, In Machine Learning, 9
  5. Buntime W., 1996. A Guide to the Literature on Learning Probabilistic Network from Data, In IEEE Transaction on KDE, vol. 8, no. 2
  6. Suzuki J., 1999. Learning Bayesian Belief Networks Based on the MDL Principle: an Efficient Algorithm Using the Branch and Bound Technique, In IEICE Trans. Inf. & Syst., Vol. E82
  7. Bouckaert R., 2002. Probabilistic Network Construction Using the Minimum Description Length Principle, Lecture Notes in Computer Science, Vol. 747
  8. Spirtes, P., Glymour, C., Scheines, R, 2001. Causation, Prediction and Search, In MIT press
  9. Cheng , J., Bell, D., Liu, W., 1997. Learning belief networks from data: an information theory based approach, In Proceedings of the Sixth ACM International Conference on Information and Knowledge Management
  10. Heckermann, D., Geiger, D., and Chickering, D., 1995. Learning Bayesian Networks. In The Combination of Knowledge and Statistical Data. Machine Learning, 20(3)
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Paper Citation


in Harvard Style

Colace F., De Santo M., Vento M. and Foggia P. (2004). BAYESIAN NETWORK STRUCTURAL LEARNING FROM DATA: AN ALGORITHMS COMPARISON . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 527-530. DOI: 10.5220/0002625305270530


in Bibtex Style

@conference{iceis04,
author={Francesco Colace and Massimo De Santo and Mario Vento and Pasquale Foggia},
title={BAYESIAN NETWORK STRUCTURAL LEARNING FROM DATA: AN ALGORITHMS COMPARISON},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={527-530},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002625305270530},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - BAYESIAN NETWORK STRUCTURAL LEARNING FROM DATA: AN ALGORITHMS COMPARISON
SN - 972-8865-00-7
AU - Colace F.
AU - De Santo M.
AU - Vento M.
AU - Foggia P.
PY - 2004
SP - 527
EP - 530
DO - 10.5220/0002625305270530