Abstract
To improve upon early detection of Classical Swine Fever, we are learning selective Naive Bayesian classifiers from data that were collected during an outbreak of the disease in the Netherlands. The available dataset exhibits a lack of distinction between absence of a clinical symptom and the symptom not having been addressed or observed. Such a lack of distinction is not uncommonly found in biomedical datasets. In this paper, we study the effect that not distinguishing between absent and non-observed features may have on the subset of features that is selected upon learning a selective classifier. We show that while the results from the filter approach to feature selection are quite robust, the results from the wrapper approach are not.
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
Edwards, S., Fukusho, A., Lefevre, P.C., et al.: Classical Swine Fever: the global situation. Veterinary Microbiology 73, 103–119 (2000)
Elbers, A.R.W., Bouma, A., Stegeman, J.A.: Quantitative assessment of clinical signs for the detection of classical swine fever outbreaks during an epidemic. Veterinary Microbiology 85, 323–332 (2002)
Elbers, A.R.W., Stegeman, J.A., Moser, H., Ekker, H.M., Smak, J.A., Pluimers, F.H.: The classical swine fever epidemic 1997-1998 in The Netherlands: descriptive epidemiology. Prev. Vet. Med. 42, 157–184 (1999)
Elvira: Elvira consortium an environment for probabilistic graphical models. In: Gómez, J., Salmerón, A. (eds.) Proceedings of the First European Workshop on Probabilistic Graphical Models, Cuenca, pp. 222–230 (2002)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Machine Learning: Proceeding of the 11th International Conference, pp. 121–129. Morgan Kaufmann Publishers, San Francisco (1994)
Kleiboeker, S.B.: Swine fever: classical swine fever and African swine fever. Vet. Clin. Food. Anim. 18, 431–451 (2002)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence Journal, 273–324 (1997)
Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pp. 399–406 (1994)
Tsamardinos, I., Aliferis, C.: Towards principled feature selection: relevancy, filters and wrappers. In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, KeyWest, Florida (2003)
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
Geenen, P., van der Gaag, L.C., Loeffen, W., Elbers, A. (2004). On the Robustness of Feature Selection with Absent and Non-observed Features. In: Barreiro, J.M., MartÃn-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_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-30547-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23964-2
Online ISBN: 978-3-540-30547-7
eBook Packages: Springer Book Archive