Abstract
This study proposes a normal distribution-based over-sampling approach to balance the number of instances belonging to different classes in a data set. The balanced training data are used to learn unbiased classifiers for the original data set. Under some conditions, the proposed over-sampling approach generates samples with expected mean and variance similar to that of the original minority class data. As the approach tries to generate synthetic data with similar probability distributions to the original data, and expands the class boundaries of the minority class, it may increase the minority class classification performance. Experimental results show that the proposed approach outperforms alternative methods on benchmark data sets most of the times when implementing several classical classification algorithms.
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
Barandela, R., Sanchez, J.S., Garcia, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36, 849–851 (2003)
Zhou, Z.-H., Liu, X.-Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18, 63–77 (2006)
Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. ACM SIGKDD Explorations Newsletter 6(1), 20–29 (2004)
Sun, A., Lim, E.-P., Liu, Y.: On Strategies for Imbalanced Text Classification Using SVM: A Comparative Study. Decision Support Systems 48(1), 191–201 (2009)
Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering, 301–312 (2006)
Yen, S.-J., Lee, Y.-S.: Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications 36, 5718–5727 (2009)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005)
Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. SIGKDD Explorations Newsletter 6, 30–39 (2004)
Estabrooks, A., Jo, T., Japkowicz, N.: A Multiple Resampling Method for Learning from Imbalanced Data Sets. Computational Intelligence 20(1), 18–36 (2004)
Peng, Y., Yao, J.: AdaOUBoost: Adaptive Over-sampling and Under-sampling to Boost the Concept Learning in Large Scale Imbalanced Data Sets. In: MIR 2010, Philadelphia, Pennsylvania, USA, pp. 111–118 (March 2010)
Drummond, C., Holte, R.C.: C4.5, Class Imbalance and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. In: Proceedings of the ICML 2003 Workshop on Learning from Imbalanced Data Sets (2003)
Zheng, Z., Wu, X., Srihari, R.: Feature Selection for Text Categorization on Imbalanced Data. SIGKDD Explorations Newsletter 6(1), 80–89 (2004)
Chen, M.-C., Chen, L.-S., Hsu, C.-C., Zeng, W.-R.: An information granulation based data mining approach for classifying imbalanced data. Information Sciences 178, 3214–3227 (2008)
Wu, G., Chang, E.Y.: KBA: Kernel boundary alignment considering imbalanced data distribution. IEEE Transactions on Knowledge and Data Engineering 17(6), 786–795 (2005)
Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 445–453. Morgan Kaufmann, San Francisco (1998)
Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided selection. In: Proceedings of the Fourteenth 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
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, H., Wang, Z. (2011). A Normal Distribution-Based Over-Sampling Approach to Imbalanced Data Classification. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-25853-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25852-7
Online ISBN: 978-3-642-25853-4
eBook Packages: Computer ScienceComputer Science (R0)