Abstract
Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases. However, these procedures typically only take into account the characteristics of each individual class. This segmented view of the data can have a negative impact. We propose a new method that uses an integrated view of the data classes to generate new examples and remove cases. ClUstered REsampling (CURE) is a method based on a holistic view of the data that uses hierarchical clustering and a new distance measure to guide the sampling procedure. Clusters generated in this way take into account the structure of the data. This enables CURE to avoid common mistakes made by other resampling methods. In particular, CURE prevents the generation of synthetic examples in dangerous regions and undersamples safe, non-borderline, regions of the majority class. We show the effectiveness of CURE in an extensive set of experiments with benchmark domains. We also show that CURE is a user-friendly method that does not require extensive fine-tuning of hyper-parameters.
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
Similar content being viewed by others
Notes
- 1.
This is a hand curated 2-dimensional data set developed to demonstrate the strengths of CURE.
References
Alcalá-Fdez, J., et al.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17, 255–287 (2011)
Barua, S., Islam, M.M., Murase, K.: A novel synthetic minority oversampling technique for imbalanced data set learning. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011. LNCS, vol. 7063, pp. 735–744. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24958-7_85
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. SIGKDD Explor. Newsl. 6(1), 20–29 (2004)
Bellinger, C., Drummond, C., Japkowicz, N.: Manifold-based synthetic oversampling with manifold conformance estimation. Mach. Learn. 107(3), 605–637 (2018)
Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. (CSUR) 49(2), 31 (2016)
Branco, P., Torgo, L., Ribeiro, R.P.: Resampling with neighbourhood bias on imbalanced domains. Expert Syst. 35(4), e12311 (2018). https://doi.org/10.1111/exsy.12311
Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 475–482. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01307-2_43
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)
Estabrooks, A., Japkowicz, N.: A mixture-of-experts framework for learning from imbalanced data sets. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds.) IDA 2001. LNCS, vol. 2189, pp. 34–43. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44816-0_4
Fernández, A., Garcia, S., Herrera, F., Chawla, N.V.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905 (2018)
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). https://doi.org/10.1007/11538059_91
He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IJCNN 2008, pp. 1322–1328. IEEE (2008)
He, H., Ma, Y.: Imbalanced Learning: Foundations, Algorithms, and Applications. Wiley, Hoboken (2013)
Jo, T., Japkowicz, N.: Class imbalances versus small disjuncts. ACM SIGKDD Explor. Newslett. 6(1), 40–49 (2004). Special issue on learning from imbalanced datasets
Kubat, M., Matwin, S., et al.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, Nashville, USA, vol. 97, pp. 179–186 (1997)
Lewis, D.D., Catlett, J., Hill, M.: Heterogeneous uncertainty sampling for supervised learning. In: International Conference on Machine Learning, pp. 148–156 (1994)
Lim, P., Goh, C.K., Tan, K.C.: Evolutionary cluster-based synthetic oversampling ensemble (eco-ensemble) for imbalance learning. IEEE Trans. Cybern. 47(9), 2850–2861 (2017)
Lin, W.C., Tsai, C.F., Hu, Y.H., Jhang, J.S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409–410, 17–26 (2017)
Nickerson, A.S., Japkowicz, N., Milios, E.: Using unsupervised learning to guide resampling in imbalanced data sets. In: Proceedings of the Eighth International Workshop on AI and Statistics, p. 5 (2001)
Oliveira, M., Torgo, L., Santos Costa, V.: Predicting wildfires. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 183–197. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46307-0_12
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Mach. Learn. 42(3), 203–231 (2001)
Rijsbergen, C.V.: Information retrieval, 2nd edition. Department of computer science, University of Glasgow (1979)
Slama, R., Wannous, H., Daoudi, M., Srivastava, A.: Accurate 3D action recognition using learning on the grassmann manifold. Pattern Recogn. 48(2), 556–567 (2015)
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Weiss, G.M.: Mining with rarity: a unifying framework. SIGKDD Explor. Newsl. 6(1), 7–19 (2004). https://doi.org/10.1145/1007730.1007734
Williams, D., Myers, V., Silvious, M.: Mine classification with imbalanced data. IEEE Geosci. Remote Sens. Lett. 6(3), 528–532 (2009)
Wu, J., Xiong, H., Chen, J.: COG: local decomposition for rare class analysis. Data Min. Knowl. Discov. 20(2), 191–220 (2010)
Yang, Q., et al.: 10 challenging problems in data mining research. Int. J. Inf. Tech. Decis. 5(4), 597–604 (2006)
Yen, S.J., Lee, Y.S.: Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst. Appl. 36(3), 5718–5727 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bellinger, C., Branco, P., Torgo, L. (2019). The CURE for Class Imbalance. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-33778-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33777-3
Online ISBN: 978-3-030-33778-0
eBook Packages: Computer ScienceComputer Science (R0)