Abstract
Compared with conventional two-class learning schemes, one-class classification simply uses a single class for training purposes. Applying one-class classification to the minorities in an imbalanced data has been shown to achieve better performance than the two-class one. In this paper, in order to make the best use of all the available information during the learning procedure, we propose a general framework which first uses the minority class for training in the one-class classification stage; and then uses both minority and majority class for estimating the generalization performance of the constructed classifier. Based upon this generalization performance measurement, parameter search algorithm selects the best parameter settings for this classifier. Experiments on UCI and Reuters text data show that one-class SVM embedded in this framework achieves much better performance than the standard one-class SVM alone and other learning schemes, such as one-class Naive Bayes, one-class nearest neighbour and neural network.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Manevitz, L.M., Yousef, M.: One-class svms for document classification. Journal of Machine Learning Research 2, 139–154 (2001)
Raskutti, B., Kowalczyk, A.: Extreme re-balancing for svms: a case study. SIGKDD Explorations 6, 60–69 (2004)
Scholkopt, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471 (2001)
Tran, Q.A., Duan, H., Li, X.: One-class support vector machine for anomaly network traffic detection. In: Proceedings of the 2nd Network Research Workshop of the 18th APAN (2004)
Kruengkrai, C., Jaruskulchai, C.: Using one-class svms for relevant sentence extraction. In: Proceedings of the 3rd International Symposium on Communications and Information Technologies (ISCIT-2003) (2003)
Chen, Y., Zhou, X.S., Huang, T.S.: One-class svm for learning in image retrieval. In: Proceedings of the International Conference in Image Processing (ICIP 2001) (2001)
Tran, Q.A., Li, X., Duan, H.: Efficient performance estimate for one-class support vector machine. Pattern Recognition Letters 26, 1174–1182 (2005)
Tran, Q.A., Zhang, Q., Li, X.: Evolving training model method for one-class svm. In: Proceeding of 2003 IEEE International Conference on Systems, Man & Cybernetics (SMC 2003) (2003)
Lunts, A., Brailovskiy, V.: Evaluation of attributes obtained in statistical decision rules. Engineering Cybernetics_ pp. 98–109 (1967)
Staelin, C.: Parameter selection for support vector machines. Technical Report HPL-2002-354R1, Hewlett-Packard Company (2003)
Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: Proc. 14th International Conference on Machine Learning, pp. 179–186. Morgan Kaufmann, San Francisco (1997)
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
Zhuang, L., Dai, H. (2006). Parameter Estimation of One-Class SVM on Imbalance Text Classification. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_46
Download citation
DOI: https://doi.org/10.1007/11766247_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34628-9
Online ISBN: 978-3-540-34630-2
eBook Packages: Computer ScienceComputer Science (R0)