Abstract
In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of noisy and borderline examples from the minority class on the classifier performance. Results showed that if data was sufficiently disturbed by these factors, then the focused re-sampling methods – NCR and our SPIDER2 – strongly outperformed the oversampling methods. They were also better for real-life data, where PCA visualizations suggested possible existence of noisy examples and large overlapping ares between classes.
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References
Chawla, N.: Data mining for imbalanced datasets: An overview. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 853–867. Springer, Heidelberg (2005)
Garcia, V., Sanchez, J., Mollineda, R.: An empirical study of the behavior of classifiers on imbalanced and overlapped data sets. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 397–406. Springer, Heidelberg (2007)
He, H., Garcia, E.: Learning from imbalanced data. IEEE Transactions on Data and Knowledge Engineering 21(9), 1263–1284 (2009)
Japkowicz, N.: Class imbalance: Are we focusing on the right issue? In: Proc. II Workshop on Learning from Imbalanced Data Sets, ICML, pp. 17–23 (2003)
Jo, T., Japkowicz, N.: Class Imbalances versus small disjuncts. SIGKDD Explorations 6(1), 40–49 (2004)
Kubat, M., Matwin, S.: Addresing the curse of imbalanced training sets: one-side selection. In: Proc. of the 14th Int. Conf. on Machine Learning, pp. 179–186 (1997)
Laurikkala, J.: Improving identification of difficult small classes by balancing class distribution. Tech. Report A-2001-2, University of Tampere (2001)
Stefanowski, J., Wilk, S.: Improving rule based classifiers induced by MODLEM by selective pre-processing of imbalanced data. In: Proc. of the RSKD Workshop at ECML/PKDD, pp. 54–65 (2007)
Stefanowski, J., Wilk, S.: Selective pre-processing of imbalanced data for improving classification performance. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 283–292. Springer, Heidelberg (2008)
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Napierała, K., Stefanowski, J., Wilk, S. (2010). Learning from Imbalanced Data in Presence of Noisy and Borderline Examples. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_18
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DOI: https://doi.org/10.1007/978-3-642-13529-3_18
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