Multiobjectivization for classifier parameter tuning
Martin Pilát, Roman Neruda · 2013
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.