Abstract
Transductive Support Vector Machine (TSVM) is a method for semi-supervised learning. In order to further improve the classification accuracy and robustness of TSVM, in this paper, we make use of self-training technique to ensemble TSVMs, and classify testing samples by majority voting. The experiment results on 6 UCI datasets show that the classification accuracy and robustness of TSVM could be improved by our approach.
This work is supported by Talent Fund of Northwest A&F University (01140402) and Young Cadreman Supporting Program of Northwest A&F University (01140301). Corresponding author: Yang Zhang.
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
Zhu, X.: Semi-supervised learning literature survey. Computer Science TR (1530), University of Wisconsin - Madison (February 2006)
Sindvani, V., Sathiya Keerthi, S.: Large Scale Semi-Supervised Linear SVMs. In: SIGIR 2006, Agust 6-11 (2006)
Brefeld, U., Scheffer, T.: Semi-Supervised Learning for Structured Output Variables. In: Proceedings of the 23rd international conference on Machine Learning, Pittshurgh, PA (to appear, 2006)
Chapelle, O., Chi, M., Zien, A.: A continuation method for semisupervised SVMs. In: ICML 2006, 23rd International Conference on Machine Learning, Pittsburgh, USA (2006)
Liu, Y., Teng, G., Yang, J.M., Wang, F.: Double Transductive Inference Algorithm For Text Classification. In: ICIC International, December 2007, pp. 1463–1469 (2007)
Zhou, Z.-H., Zhan, D.-C., Yang, Q.: Semi-supervised learning with very few labeled training examples. In: Twenty-Second AAAI Conference on Artificial Intelligence, AAAI 2007 (2007)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, p. 1. Springer, Heidelberg (2000)
Polokar, R.: Ensemble based systems in decision making. IEEE Circuits and System Magazine (2006)
Lei, Z., Yang, Y., Wu, Z.: Ensemble of Support Vector Machine for Text-Independent Speaker Recognition. International Journal of Computer Science and Network Security (May 2006)
Lin, H.-T., Li, L.: Infinite ensemble learning with support vector machines. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 242–254. Springer, Heidelberg (2005)
Dong, Y.-S., Han, K.-S.: Text Classification Based On Data Partitioning and Parameter Varying Ensemble. In: SAC 2005 (March 13-17, 2005)
Perdisci, R., Gu, G., Lee, W.: Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection System. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065. Springer, Heidelberg (2006)
Zhou, Z.H., Li, M.: Tri-Training: Exploiting unlabeled data using three classifiers. IEEE Trans. on Knowledge and Data Engineering 17(11), 1529–1541 (2005)
ChapelleVikas, O., Keerthi, S.S.: Optimization Techniques for Semi-Supervised Support Vector Machines. Journal of Machine Learning Research 9, 203–233 (2008)
Joachims, T.: Transductive Inference for Text Classification using Support Vector Machines. In: International Conference on Machine Learning, ICML (1999)
Stepenosky, N., Green, D., Kounios, J., Clark, C.M., Polikar, R.: Majority Vote and Decision Template Based Ensemble Classifiers Trained on Event Related Potentials for Early Diagnosis of Alzheimer’s Disease. In: ICASSP 2006 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, T., Zhang, Y. (2008). Improving Transductive Support Vector Machine by Ensembling. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-89378-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89377-6
Online ISBN: 978-3-540-89378-3
eBook Packages: Computer ScienceComputer Science (R0)