Abstract
SuperParent-One-Dependence Estimators (SPODEs) loosen Naive-Bayes’ attribute independence assumption by allowing each attribute to depend on a common single attribute (superparent) in addition to the class. An ensemble of SPODEs is able to achieve high classification accuracy with modest computational cost. This paper investigates how to select SPODEs for ensembling. Various popular model selection strategies are presented. Their learning efficacy and efficiency are theoretically analyzed and empirically verified. Accordingly, guidelines are investigated for choosing between selection criteria in differing contexts.
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
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control AC-19, 716–723 (1974)
Blake, C., Merz, C.J.: UCI repository of machine learning databases [Machine-readable data repository]. University of California, Department of Information and Computer Science, Irvine, CA (2004)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29V(2-3), 131–163 (1997)
Keogh, E., Pazzani, M.: Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. In: Proceedings of the International Workshop on Artificial Intelligence and Statistics, pp. 225–230 (1999)
Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. Chapman & Hall/CRC, Boca Raton (2004)
Sahami, M.: Learning limited dependence Bayesian classifiers. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 334–338. AAAI Press, Menlo Park (1996)
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–465 (1978)
Suzuki, J.: Learning Bayesian belief networks based on the MDL principle: an efficient algorithm using the branch and bound technique. In: Proceedings of the International Conference on Machine Learning, pp. 463–470 (1996)
Webb, G.I., Boughton, J., Wang, Z.: Not so naive Bayes: Averaged one-dependence estimators. Machine Learning 58, 5–24 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Y., Korb, K., Ting, K.M., Webb, G.I. (2005). Ensemble Selection for SuperParent-One-Dependence Estimators. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_13
Download citation
DOI: https://doi.org/10.1007/11589990_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
eBook Packages: Computer ScienceComputer Science (R0)