Abstract
In the field of neural networks, feature selection has been studied for the last ten years and classical as well as original methods have been employed. This paper reviews the efficiency of four approaches to do a driven forward features selection on neural networks . We assess the efficiency of these methods compare to the simple Pearson criterion in case of a regression problem.
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
JMLR (ed.): Special Issue on Variable and Feature Selection. Journal of Machine Learning Research 3(Mar) (2003)
Guyon, I., Elisseef, A.: An introduction to variable and feature selection. JMLR 3(MAR), 1157–1182 (2003)
Guyon, I.: To appear - Feature extraction, foundations and applications (2006)
Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2) (1997)
Thomson, M.L.: Selection of variables in multiple regression part i: A review and evaluation and part ii: Chosen procedures, computations and examples. International Statistical Review 46, 1–19, 129–146 (1978)
McLachlan, G.: Discriminant Analysis and Statistical Pattern Recognition. Wiley-Interscience publication, Chichester (1992)
Leray, P., Gallinari, P.: Feature selection with neural networks. Technical report, LIP6 (1998)
Miller, A.J.: Subset Selection in Regression. Chapman and Hall, Boca Raton (1990)
Lemaire, V., Clérot, C.: An input variable importance definition based on empirical data probability and its use in variable selection. In: International Joint Conference on Neural Networks IJCNN (2004)
Féraud, R., Clérot, F.: A methodology to explain neural network classification. Neural Networks 15, 237–246 (2002)
Breiman, L.: Random forest. Machine Learning 45 (2001)
Yacoub, M., Bennani, Y.: Hvs: A heuristic for variable selection in multilayer artificial neural network classifier. In: ANNIE, pp. 527–532 (1997)
Moody, J.: Prediction Risk and Architecture Selection for Neural Networks. From Statistics to Neural Networks-Theory and Pattern Recognition. Springer (1994)
Ruck, D.W., Rogers, S.K., Kabrisky, M.: Feature selection using a multilayer perceptron. J. Neural Network Comput. 2(2), 40–48 (1990)
Réfénes, A.N., Zapranis, A., Utans, J.: Stock performance using neural networks: A comparative study with regression models. Neural Network 7, 375–388 (1994)
Refenes, A., Zapranis, A., Utans, J.: Neural model identification, variable selection and model adequacy. In: Neural Networks in Financial Engineering, Proceedings of NnCM 1996 (1996)
Burkitt, A.N.: Refined pruning techniques for feed-forward neural networks. Complex System 6, 479–494 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lemaire, V., Féraud, R. (2006). Driven Forward Features Selection: A Comparative Study on Neural Networks. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_77
Download citation
DOI: https://doi.org/10.1007/11893257_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)