Model Selection in an Ensemble Framework
Joerg Wichard · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
We like to present a method to build ensemble models based on an extended cross-validation approach. The cross-validation puts several model classes in a tournament and selects the best performing model with respect to the validation set. This leads to a model selection strategy and an estimation of the expected modelling error.