Multi-objectivization and Surrogate Modelling for Neural Network Hyper-parameters Tuning

Abstrakt

We present a multi-objectivization 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 data sets and compare it to a surrogate based single-objective algorithm for the same problem. Moreover, we compare the multi-objectivization approach to two surrogate based approaches – a singleobjective one and a multi-objective one.

Klíčová slova

• Multi-objective optimization
• parameter tuning
• neural networks
• surrogate modelling
• multi-objectivization