Abstract
Decorrelated and CELS are two ensembles that modify the learning procedure to increase the diversity among the networks of the ensemble. Although they provide good performance according to previous comparatives, they are not as well known as other alternatives, such as Bagging and Boosting, which modify the learning set in order to obtain classifiers with high performance. In this paper, two different procedures are introduced to Decorrelated and CELS in order to modify the learning set of each individual network and improve their accuracy. The results show that these two ensembles are improved by using the two proposed methodologies as specific set generators.
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
Asuncion, A., Newman, D.: UCI machine learning repository, University of California, Irvine, School of Information and Computer Sciences (2007)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Fernández-Redondo, M., Hernández-Espinosa, C., Torres-Sospedra, J.: Multilayer feedforward ensembles for classification problems. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 744–749. Springer, Heidelberg (2004)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Hernández-Espinosa, C., Torres-Sospedra, J., Fernández-Redondo, M.: New experiments on ensembles of multilayer feedforward for classification problems. In: Proceedings of IJCNN 2005, pp. 1120–1124 (2005)
Liu, Y., Yao, X.: Simultaneous training of negatively correlated neural networks in an ensemble. IEEE T. Syst. Man. Cyb. 29, 716 (1999)
Parmanto, B., Munro, P.W., Doyle, H.R.: Improving committee diagnosis with resampling techniques. In: Advances in Neural Information Processing Systems, pp. 882–888 (1996)
Rosen, B.E.: Ensemble learning using decorrelated neural networks. Connection Science 8(3-4), 373–384 (1996)
Torres-Sospedra, J., Hernández-Espinosa, C., Fernández-Redondo, M.: Adaptive boosting: Dividing the learning set to increase the diversity and performance of the ensemble. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 688–697. Springer, Heidelberg (2006)
Torres-Sospedra, J.: Ensembles of Artificial Neural Networks: Analysis and Development of Design Methods. Ph.D Thesis, Universitat Jaume I (2011)
Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connection Science 8(3-4), 385–403 (1996)
Yildiz, O.T., Alpaydin, E.: Ordering and finding the best of k>2 supervised learning algorithms. IEEE T. Pattern. Anal. 28(3), 392–402 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Torres-Sospedra, J., Hernández-Espinosa, C., Fernández-Redondo, M. (2011). Using Bagging and Cross-Validation to Improve Ensembles Based on Penalty Terms. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-24958-7_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
Online ISBN: 978-3-642-24958-7
eBook Packages: Computer ScienceComputer Science (R0)