Abstract
Missing data is a common issue in almost every real-world dataset. In this work, we investigate the relative merits of applying two imputation schemes for coping with this problem while designing radial basis function network classifiers, which show sensitiveness to the existence of missing values. Whereas the first scheme centers upon the k-nearest neighbor algorithm and has been deployed with success in other supervised/unsupervised learning contexts, the second is based on a simple genetic algorithm model and has not been fully explored so far.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Acuna, E., Rodriguez, C.: The treatment of missing values and its effect in the classifier accuracy. In: Banks, D., et al. (eds.) Classification, Clustering and Data Mining Applications, pp. 639–648. Springer, Heidelberg (2004)
Asunción, A., Newman, D.J.: UCI Machine Learning Repository. University of California at Irvine (1998), http://ics.uci.edu/~mlearn/MLRepository.html
Batista, G.E., Monard, M.C.: An analysis of four missing data treatment methods for supervised learning. App. Artif. Intel. 17(5), 519–533 (2003)
Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Heidelberg (2002)
Grzymała-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 378–385. Springer, Heidelberg (2001)
Harpham, C., Dawson, W., Brown, R.: A review of genetic algorithms applied to training radial basis function networks. Neural Comp. & App. 13(3), 193–201 (2004)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1998)
Hruschka, E.R., Hruschka Jr., E.R., Ebecken, N.F.F.: Missing values imputation for a clustering genetic algorithm. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 245–254. Springer, Heidelberg (2005)
Liu, P., Lei, L.: A review of missing data treatment methods. Int. Journal of Intel. Inf. Manag. Syst. and Tech. 1(3), 412–419 (2005)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Oliveira, P.G., Coelho, A.L.V. (2009). Genetic Versus Nearest-Neighbor Imputation of Missing Attribute Values for RBF Networks. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-03040-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
eBook Packages: Computer ScienceComputer Science (R0)