Abstract
Classifying imbalanced data is a big challenge for machine learning techniques, especially for medical data. To deal with this challenge, many solutions have been proposed. The most famous methods are based on the Synthetic Minority Over-sampling Technique (SMOTE), which creates new synthetic instances in the minority class. In this paper, we study the efficiency of the SMOTE-based methods on some imbalanced data sets. We then propose extending these techniques with Active Learning to control the evolution of the minority class better. Active Learning uses uncertainty and diversity sampling to choose wisely the data points from which the synthetic samples will be generated. To evaluate our approach, we make comprehensive experimental studies on two medical data sets for diabetes diagnosis and breast cancer diagnosis.
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
References
Aggarwal, C.C., Kong, X., Gu, Q., Han, J., Philip, S.Y.: Active learning: a survey. In: Data Classification, pp. 599–634. Chapman and Hall (2014)
Bach, F.R., Heckerman, D., Horvitz, E.: Considering cost asymmetry in learning classifiers. J. Mach. Learn. Res. 7, 1713–1741 (2006)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Special issue on learning from imbalanced data sets. ACM SIGKDD Explor. Newsl. 6(1), 1–6 (2004)
Chen, B., Xia, S., Chen, Z., Wang, B., Wang, G.: RSMOTE: a self-adaptive robust smote for imbalanced problems with label noise. Inf. Sci. 553, 397–428 (2021). https://doi.org/10.1016/j.ins.2020.10.013
Devarriya, D., Gulati, C., Mansharamani, V., Sakalle, A., Bhardwaj, A.: Unbalanced breast cancer data classification using novel fitness functions in genetic programming. 140, 112866. https://doi.org/10.1016/j.eswa.2019.112866
Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
Elreedy, D., Atiya, A.F.: A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf. Sci. 505, 32–64 (2019)
Ertekin, S., Huang, J., Giles, C.L.: Active learning for class imbalance problem. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 823–824. ACM (2007). https://doi.org/10.1145/1277741.1277927
Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets, vol. 10. Springer, Cham (2018)
Fernandez, A., Garcia, S., Herrera, F., Chawla, N.V.: SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. 61, 863–905 (2018). https://doi.org/10.1613/jair.1.11192
Ben Hamida, S., Benjelloun, G., Hmida, H.: Trends of evolutionary machine learning to address big data mining. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Arduin, P.-E. (eds.) ICIKS 2021. LNBIP, vol. 425, pp. 85–99. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85977-0_7
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: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328 (2008)
Hmida, H., Hamida, S.B., Borgi, A., Rukoz, M.: Sampling methods in genetic programming learners from large datasets: a comparative study. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds.) INNS 2016. AISC, vol. 529, pp. 50–60. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47898-2_6
Le, T., Vo, M.T., Vo, B., Lee, M.Y., Baik, S.W.: A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction. Complexity 2019 (2019)
Li, J., et al.: SMOTE-NaN-DE: addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution. Knowl.-Based Syst. 223, 107056 (2021)
Oh, S., Lee, M.S., Zhang, B.T.: Ensemble learning with active example selection for imbalanced biomedical data classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(2), 316–325 (2010). https://doi.org/10.1109/TCBB.2010.96
Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C.: Reducing misclassification costs. In: Machine Learning Proceedings, pp. 217–225. Elsevier (1994)
Saez, J.A., Luengo, J., Stefanowski, J., Herrera, F.: SMOTE-IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf. Sci. 291, 184–203 (2015). https://doi.org/10.1016/j.ins.2014.08.051
Xu, Z., Shen, D., Nie, T., Kou, Y., Yin, N., Han, X.: A cluster-based oversampling algorithm combining smote and k-means for imbalanced medical data. Inf. Sci. 572, 574–589 (2021)
Zhang, J., Wu, X., Shengs, V.S.: Active learning with imbalanced multiple noisy labeling. IEEE Trans. Cybern. 45(5), 1095–1107 (2015). https://doi.org/10.1109/TCYB.2014.2344674
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sena, R., Ben Hamida, S. (2024). ACTIVE SMOTE for Imbalanced Medical Data Classification. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Chakhar, S., Williams, N., Haig, E. (eds) Advances in Information Systems, Artificial Intelligence and Knowledge Management. ICIKS 2023. Lecture Notes in Business Information Processing, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-51664-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-51664-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51663-4
Online ISBN: 978-3-031-51664-1
eBook Packages: Computer ScienceComputer Science (R0)