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.
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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).
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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
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