Abstract
In this paper an incremental version of the ANOVA and Functional Networks Feature Selection (AFN-FS) method is presented. This new wrapper method (IAFN-FS) is based on an incremental functional decomposition, thus eliminating the main drawback of the basic method: the exponential complexity of the functional decomposition. This complexity limited its scope of applicability, being only applicable to datasets with a relatively small number of features. The performance of the incremental version of the method was tested against several real data sets. The results show that IAFN-FS outperforms the accuracy obtained by other standard and novel feature selection methods, using a small set of features.
The authors wish to acknowledge Xunta de Galicia for partial funding under project PGIDT05TIC10502PR.
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
Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: Sensitivity Analysis in practice: A guide to assessing scientific models. John Wiley & Sons, Chichester (2004)
Bishop, C.: Neural Networks for Patter Recognition. Oxford University Press, New York (1995)
Castillo, E., Guijarro-Berdiñas, B., Fontenla-Romero, O., Alonso-Betanzos, A.: A very fast learning method for neural networks based on sensitivity analysis. Journal of Machine Learning Research 7, 1159–1182
Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intelligence, Special issue on relevance 97(1-2), 273–324 (1997)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research, Special issue on Variable and Feature Selection 3, 1157–1182 (2003)
Blum, A.L., Langley, P.: Selection of relevance features and examples in machine learning. Artificial Intelligence, Special issue on relevance 97(1-2), 245–271 (1997)
Sánchez-Maroño, N., Caamaño-Fernández, M., Castillo, E., Alonso-Betanzos, A.: Functional networks and analysis of variance for feature selection. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 1031–1038. Springer, Heidelberg (2006)
Castillo, E., Sánchez-Maroño, N., Alonso-Betanzos, A., Castillo, M.: Functional network topology learning and sensitivity analysis based on anova decomposition. Neural Computation 19(1) (2007)
Sobol, I.M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 55, 271–280 (2001)
Sivagaminathan, R.K., Ramakrisham, S.: A hybrid approach for feature subset selection using neural networks and ant colony optimization. Experts systems with applications 33, 49–60 (2007)
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature extraction. Foundations and applications (2006)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sánchez-Maroño, N., Alonso-Betanzos, A. (2007). Feature Selection Based on Sensitivity Analysis. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-75271-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75270-7
Online ISBN: 978-3-540-75271-4
eBook Packages: Computer ScienceComputer Science (R0)