A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
A practical SVM-based algorithm for ordinal regression in image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Support Vector Ordinal Regression
Neural Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
The genetic algorithm for breast tumor diagnosis-The case of DNA viruses
Applied Soft Computing
Applied Soft Computing
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Evaluation Measures for Ordinal Regression
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
Ordinal extreme learning machine
Neurocomputing
An experimental study of different ordinal regression methods and measures
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Liver transplantation is nowadays a widely-accepted treatment for patients who present a terminal liver disease. Nevertheless, transplantation is greatly hampered by the un-availability of suitable liver donors; several methods have been developed and applied to find a better system to prioritize recipients on the waiting list, although most of them only consider donor or recipient characteristics (but not both). This paper proposes a novel donor-recipient liver allocation system constructed to predict graft survival after transplantation by means of a dataset comprised of donor-recipient pairs from different centres (seven Spanish and one UK hospitals). The best model obtained is used in conjunction with the Model for End-stage Liver Disease score (MELD), one of the current assignation methodology most used globally. This problem is assessed using the ordinal regression learning paradigm due to the natural ordering in the classes of the problem, via a cascade binary decomposition methodology and the Support Vector Machine methodology. The methodology proposed has shown competitiveness in all the metrics selected, when compared to other machine learning techniques and efficiently complements the MELD score based on the principles of efficiency and equity. Finally, a simulation of the proposal is included, in order to visualize its performance in realistic situations. This simulation has shown that there are some determining factors in the characterization of the survival time after transplantation (concerning both donors and recipients) and that the joint use of these sets of information could be, in fact, more useful and beneficial for the survival principle. Nonetheless, the results obtained indicate the true complexity of the problem dealt within this study and the fact that other characteristics that have not been included in the dataset may be of importance for the characterization of the dependent variable (survival time after transplantation), thus starting a promising line of future work.