Abstract
In this paper, a new algorithm called Averaged Conservative Boosting (ACB) is presented to build ensembles of neural networks. In ACB we mix the improvements that Averaged Boosting (Aveboost) and Conservative Boosting (Conserboost) made to Adaptive Boosting (Adaboost). In the algorithm we propose we have applied the conservative equation used in Conserboost along with the averaged procedure used in Aveboost in order to update the sampling distribution used in the training of Adaboost. We have tested the methods with seven databases from the UCI repository. The results show that the best results are provided by our method, Averaged Conservative Boosting.
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
Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connection Science 8(3-4), 385–403 (1996)
Raviv, Y., Intratorr, N.: Bootstrapping with noise: An effective regularization technique. Connection Science, Special issue on Combining Estimators 8, 356–372 (1996)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Breiman, L.: Arcing classifiers. The Annals of Statistics 26(3), 801–849 (1998)
Kuncheva, L., Whitaker, C.J.: Using diversity with three variants of boosting: Aggressive. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, Springer, Heidelberg (2002)
Oza, N.C.: Boosting with averaged weight vectors. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 15–24. Springer, Heidelberg (2003)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: 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
Torres-Sospedra, J., Hernández-Espinosa, C., Fernández-Redondo, M. (2007). Averaged Conservative Boosting: Introducing a New Method to Build Ensembles of Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-74690-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74689-8
Online ISBN: 978-3-540-74690-4
eBook Packages: Computer ScienceComputer Science (R0)