| 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 |
Lecture Notes in Computer Science |
| 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 |
| Universidad Loyola members |
|
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