Abstract
In order to employ machine learning in realistic clinical settings we are in need of algorithms which show robust performance, producing results that are intelligible to the physician. In this article, we present a new Bayesian-network learning algorithm which can be deployed as a tool for learning Bayesian networks, aimed at supporting the processes of prognosis or diagnosis. It is based on a maximum (conditional) mutual information criterion. The algorithm is evaluated using a high-quality clinical dataset concerning disorders of the liver and biliary tract, showing a performance which exceeds that of state-of-the-art Bayesian classifiers. Furthermore, the algorithm places less restrictions on classifying Bayesian network structures and therefore allows easier clinical interpretation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lucas, P.J.F., van der Gaag, L., Abu-Hanna, A.: Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine 30, 201–214 (2004)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)
Chickering, D.M., Geiger, D., Heckerman, D.: Learning Bayesian networks is NPhard. Technical report, Microsoft Research (1994)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, Chichester (1973)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
van Dijk, S., van der Gaag, L.C., Thierens, D.: A skeleton-based approach to learning Bayesian networks from data. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H., et al. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 132–143. Springer, Heidelberg (2003)
Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence 137, 43–90 (2002)
Lucas, P.J.F.: Restricted Bayesian network structure learning. In: Gâmez, J., Moral, S., Salmeron, A. (eds.) Advances in Bayesian Networks, Studies in Fuzziness and Soft Computing, vol. 146, pp. 217–232. Springer, Berlin (2004)
Malchow-Møller, A., Thomson, C., Matzen, P., et al.: Computer diagnosis in jaundice: Bayes’ rule founded on 1002 consecutive cases. J. Hepatol. 3, 154–163 (1986)
Lindberg, G., Thomson, C., Malchow-Møller, A., Matzen, P., Hilden, J.: Differential diagnosis of jaundice: applicability of the Copenhagen pocket diagnostic chart proven in Stockholm patients. Liver 7, 43–49 (1987)
Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proceedings of UAI 1994 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
van Gerven, M., Lucas, P. (2004). Employing Maximum Mutual Information for Bayesian Classification. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds) Biological and Medical Data Analysis. ISBMDA 2004. Lecture Notes in Computer Science, vol 3337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30547-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-30547-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23964-2
Online ISBN: 978-3-540-30547-7
eBook Packages: Springer Book Archive