GA-stacking: Evolutionary stacked generalization

Stacking is a widely used technique for combining classifier and improving prediction accuracy. Early research in Stacking showed that selecting the right classifiers their parameters and the meta-classifier was a critical issue. Most of the research on this topic hand picks the right combination of classifier and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good Stacking configurations Since this can lead to overfitting one of the goals of this paper is to empirically evaluate the overall efficien y of the approach. A second goal is to compare our approach with the current best Stacking building techniques. The results show that our approach find Stacking configuration that, in the worst case, perform as well as the best techniques, with the advantage of not having to manually set up the structure of the Stacking system.