An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Journal of Global Optimization
An introduction to variable and feature selection
The Journal of Machine Learning Research
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
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This paper constructs a hybrid, multi-objective and evolutionary algorithm based on Differential Evolutions using neural network models and q-Gaussian basis units in order to develop an efficient and complete system for donor-recipient assignment in liver transplantation. The algorithm is used for the classification of a binary dataset and will predict graft survival at 15 and 90 days after the transplantation. Other hybrid approaches combining artificial neural networks with evolutionary computation and well-known algorithms are presented in order to compare the obtained performance of both mono and multi-objective methods, using other methods such as Support Vector Machines and Discriminant Analysis. Some supervised attribute selection methods were previously applied, in order to extract the most discriminant variables in the problem presented. The models obtained allowed medical experts to predict survival rates and to come to a fair decision based on the principles of justice, efficiency and equity.