Abstract
Bayesian networks have been widely applied to the feature selection problem. The proposed methods learn a Bayesian network from the available dataset and utilize the Markov Blanket of the target feature to select the relevant features. And we apply an artificial immune system as search procedure to the Bayesian network learning problem. It find suitable Bayesian networks that best fit the dataset. Due to the resulting multimodal search capability, several subsets of features are obtained. Experimental results were carried out in order to evaluate the proposed methodology in classification problems and the subsets of features were produced.
An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-642-04020-7_119
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
References
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)
Koller, D., Sahami, M.: Toward Optimal Feature Selection. Int. Conf. on Machine Learning, 284–292 (1996)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recogn. Lett. 10(5), 335–347 (1989)
Castro, P.A.D., Von Zuben, F.J.: Feature subset selection by means of a Bayesian Artificial Immune System. In: Proc. of the 8th Int. Conf. on Hybrid Intelligent Systems, pp. 561–566 (2008)
Inza, I., Larraaga, P., Etxeberria, R., Sierra, B.: Feature subset selection by Bayesian network-based optimization. Artificial Intelligence 123, 157–184 (2000)
Almuallim, H., Dietterich, T.G.: Learning with many irrelevant features. In: Proc. of the 9th National Conf. on Artificial Intelligence, vol. 2, pp. 547–552 (1991)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
Chickering, D., Heckerman, D., Meek, C.: Large-sample learning of Bayesian networks is NP-Hard. Journal of Machine Learning Research 5, 1287–1330 (2004)
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–465 (1978)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository
Hruschka Jr, E.R., Hruschka, E.R., Ebecken, N.F.F.: Feature selection by bayesian networks. In: Tawfik, A.Y., Goodwin, S.D. (eds.) Canadian AI 2004. LNCS (LNAI), vol. 3060, pp. 370–379. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, B. (2009). Retracted: Using Bayesian Network and AIS to Perform Feature Subset Selection. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-04020-7_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
eBook Packages: Computer ScienceComputer Science (R0)