Abstract
Nowadays imbalanced learning represents one of the most vividly discussed challenges in machine learning. In these scenarios, one or some of the classes in the problem have a significantly lower a priori probability, usually leading to trivial or non-desirable classifiers. Because of this, imbalanced learning has been researched to a great extent by means of different approaches. Recently, the focus has switched from binary classification to other paradigms where imbalanced data also arise, such as ordinal classification. This paper tests the application of learning pairwise ranking with multiple granularity levels in an ordinal and imbalanced classification problem where the aim is to construct an accurate model for donor-recipient allocation in liver transplantation. Our experiments show that approaching the problem as ranking solves the imbalance issue and leads to a competitive performance.
This work has been subsidized by the TIN2014-54583-C2-1-R and the TIN2015-70308-REDT projects of the Spanish Ministerial Commission of Science and Technology (MINECO, Spain), FEDER funds (EU), the PI-0312-2014 project of the “Fundación pública andaluza progreso y salud” (Spain), the PI15/01570 project (“Proyectos de Investigación en Salud”), and also by NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016.
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
Notes
- 1.
Note, however, that a simple regression analysis is not feasible because of the high number of organs which survived the 365 day threshold (for which, we do not have more information).
References
Baccianella, S., Esuli, A., Sebastiani, F.: Evaluation measures for ordinal regression. In: Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, pp. 283–287 (2009)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd international Conference on Machine Learning - ICML 2005, pp. 89–96. ACM, New York (2005)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)
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)
Chu, W., Keerthi, S.S.: Support vector ordinal regression. Neural Comput. 19, 792–815 (2007)
Cruz, R., Fernandes, K., Cardoso, J.S., Costa, J.F.P.: Tackling class imbalance with ranking. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2182–2187, July 2016
Cruz, R., Fernandes, K., Pinto Costa, J.F., Perez Ortiz, M., Cardoso, J.S.: Ordinal class imbalance with ranking. In: Rojas, I., et al. (eds.) IWANN 2017, Part II. LNCS, vol. 10306, pp. 538–548. Springer, Cham (2017)
Cruz-Ramírez, M., Hervás-Martínez, C., Sánchez-Monedero, J., Gutiérrez, P.A.: Metrics to guide a multi-objective evolutionary algorithm for ordinal classification. Neurocomputing 135, 21–31 (2014)
Fürnkranz, J., Hüllermeier, E., Vanderlooy, S.: Binary decomposition methods for multipartite ranking. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 359–374. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04180-8_41
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 463–484 (2012)
Gutiérrez, P.A., Pérez-Ortiz, M., Sánchez-Monedero, J., Fernandez-Navarro, F., Hervás-Martínez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2016)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 9(21), 1263–1284 (2009)
Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: International Conference on Artificial Neural Networks, pp. 97–102 (1999)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, pp. 115–132. MIT Press, Cambridge (2000)
Hsu, C.W., Lin, C.J.: A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)
Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429–449 (2002)
McCullagh, P.: Regression models for ordinal data. J. Roy. Stat. Soc. 42(2), 109–142 (1980)
Pérez-Ortiz, M., Cruz-Ramírez, M., Ayllón-Terán, M., Heaton, N., Ciria, R., Hervás-Martínez, C.: An organ allocation system for liver transplantation based on ordinal regression. Appl. Soft Comput. 14(Part A), 88–98 (2014)
Pérez-Ortiz, M., Gutiérrez, P., Hervás-Martínez, C., Yao, X.: Graph-based approaches for over-sampling in the context of ordinal regression. IEEE Trans. Knowl. Data Eng. 27(5), 1233–1245 (2015)
Pérez-Ortiz, M., Gutiérrez, P.A., Tino, P., Hervás-Martínez, C.: Oversampling the minority class in the feature space. IEEE Trans. Neural Netw. Learn. Syst. 27(9), 1947–1961 (2016)
Rajaram, S., Agarwal, S.: Generalization bounds for k-partite ranking. In: Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS2005), pp. 28–23 (2005)
Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 281–288 (2009)
Torgo, L., Ribeiro, R.P., Pfahringer, B., Branco, P.: SMOTE for regression. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) EPIA 2013. LNCS (LNAI), vol. 8154, pp. 378–389. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40669-0_33
Zheng, Z., Chen, K., Sun, G., Zha, H.: A regression framework for learning ranking functions using relative relevance judgments. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 287–294. ACM (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Pérez-Ortiz, M., Fernandes, K., Cruz, R., Cardoso, J.S., Briceño, J., Hervás-Martínez, C. (2017). Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-59147-6_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59146-9
Online ISBN: 978-3-319-59147-6
eBook Packages: Computer ScienceComputer Science (R0)