Abstract
Imbalanced datasets often pose challenges in classification problems. In this work we study and quantify the problem of imbalanced classification using support vector machines (SVM). We identify the conditions under which a SVM failure occur, both theoretically and experimentally, and show that it can be relevant even in cases of very weakly imbalanced data. The guidelines for exploratory data analysis are presented to avoid the SVM failure.
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
References
Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_7
Batuwita, R., Palade, V.: FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Trans. Fuzzy Syst. 18(3), 558–571 (2010)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011). https://doi.org/10.1145/1961189.1961199
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1023/A:1022627411411
Illan, I.A., et al.: Automated detection and segmentation of nonmass-enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging (2018). https://www.hindawi.com/journals/cmmi/2018/5308517/
Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Trans. Neural Netw. 13(2), 464–471 (2002)
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013). http://www.sciencedirect.com/science/article/pii/S0020025513005124
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000). https://doi.org/10.1162/089976600300015565
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
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
Illan, I.A. et al. (2019). Support Vector Machine Failure in Imbalanced Datasets. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-19591-5_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19590-8
Online ISBN: 978-3-030-19591-5
eBook Packages: Computer ScienceComputer Science (R0)