Abstract
In this paper a method for feature selection based on analysis of variance and using functional networks as induction algorithm is presented. It follows a backward selection search, but several features are discarded in the same step. The method proposed is compared with two SVM based methods, obtaining a smaller set of features with a similar accuracy.
This work has been partially funded by the Spanish Ministry of Science and Technology under project TIC-2003-00600 with FEDER funds.
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References
Liu, Y., Zheng, Y.F.: A novel feature selection method for support vector machines. Pattern Recognition 39, 1333–1345 (2006)
Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intelligence journal, special issue on relevance 97(1-2), 273–324 (1997)
Castillo, E., Cobo, A., Gutiérrez, J.M., Pruneda, E.: Functional Networks with Applications. Kluwer Academic Publishers, Dordrecht (1998)
Castillo, E., Gutiérrez, J.M., Hadi, A.S., Lacruz, B.: Some applications of functional networks in statistics and engineering. Technometrics 43(1), 10–24 (2001)
Sobol, I.M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 55, 271–280 (2001)
Sanchez-Maroño, N., Alonso-Betanzos, A., Castillo, E.: A new wrapper method for feature subset selection. In: Proc. European Symp. on Artificial Neural Networks, pp. 515–520 (2005)
Bishop, C.: Neural Networks for Patter Recognition. Oxford University Press, New York (1995)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Wang, L. (ed.): Support Vector Machines: Theory and Applications. Springer, New York (2005)
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Sánchez-Maroño, N., Caamaño-Fernández, M., Castillo, E., Alonso-Betanzos, A. (2006). Functional Networks and Analysis of Variance for Feature Selection. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_123
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DOI: https://doi.org/10.1007/11875581_123
Publisher Name: Springer, Berlin, Heidelberg
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