Title Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem
Authors Dorado-Moreno, Manuel , PÉREZ ORTIZ, MARÍA, Dolores Ayllon-Teran, Maria , Antonio Gutierrez, Pedro , Hervas-Martinez, Cesar
External publication No
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.339
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964068451&doi=10.1007%2f978-3-319-32034-2_38&partnerID=40&md5=85f44c23fe564f24801f82bdd14bc0d2
Publication date 01/01/2016
ISI 000389499600038
Scopus Id 2-s2.0-84964068451
DOI 10.1007/978-3-319-32034-2_38
Abstract Ordinal regression considers classification problems where there exists a natural ordering among the categories. In this learning setting, thresholds models are one of the most used and successful techniques. On the other hand, liver transplantation is a widely-used treatment for patients with a terminal liver disease. This paper considers the survival time of the recipient to perform an appropriate donor-recipient matching, which is a highly imbalanced classification problem. An artificial neural network model applied to ordinal classification is used, combining evolutionary and gradient-descent algorithms to optimize its parameters, together with an ordinal over-sampling technique. The evolutionary algorithm applies a modified fitness function able to deal with the ordinal imbalanced nature of the dataset. The results show that the proposed model leads to competitive performance for this problem.
Keywords Ordinal regression; Artificial neural networks; Imbalanced classification; Liver transplantation; Donor-recipient matching
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