FISA: Feature-Based Instance Selection for Imbalanced Text Classification
Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (Feature-based Instance Selection Algorithm), is proposed to select only a subset of negative training documents for training a SVM classifier. With a smaller carefully selected training set, a SVM classifier can be more efficiently trained while delivering comparable or better classification accuracy. In our experiments on the 20-Newsgroups dataset, using only 35% negative training examples and 60% learning time, methods based on FISA delivered much better classification accuracy than those methods using all negative training documents.
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- 1.Brank, J., Grobelnik, M., Milic-Frayling, N., Mladenic, D.: Training text classifiers with SVM on very few positive examples. Technical Report MSR-TR-2003-34, Microsoft Research (April 2003)Google Scholar
- 3.Chen, C.-M., Lee, H.-M., Kao, M.-T.: Multi-class SVM with negative data selection for web page classification. In: Proc. of IEEE Joint Conf. on Neural Networks, Budapest, Hungary, pp. 2047–2052 (2004)Google Scholar
- 5.Fragoudis, D., Meretakis, D., Likothanassis, S.: Integrating feature and instance selection for text classification. In: Proc. of ACM SIGKDD 2002, Canada, pp. 501–506 (2002)Google Scholar
- 6.Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: Onesided selection. In: Proc. of ICML 1997, pp. 179–186 (1997)Google Scholar
- 11.Wu, G., Chang, E.Y.: Kba: Kernel boundary alignment considering imbalanced data distribution. IEEE TKDE 17(6), 786–795 (2005)Google Scholar