Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The kappa statistic: a second look
Computational Linguistics
A mono surrogate for multiobjective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Meta-learning for evolutionary parameter optimization of classifiers
Machine Learning
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We present a multiobjectivization approach to the parameter tuning of RBF networks and multilayer perceptrons. The approach works by adding two new objectives -- maximization of kappa statistic and minimization of root mean square error -- to the originally single-objective problem of minimizing the classification error of the model. We show the performance of the multiobjectivization approach on five datasets.