Stacking is a widely used technique for combining classifiers and improving prediction
accuracy. Early research in Stacking showed that selecting the right classifiers, their
parameters and the meta-classifiers was a critical issue. Most of the research on this topic
hand picks the right combination of classifiers 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 …

Stacking is a widely used technique for combining classifiers and improving prediction
accuracy. Early research in Stacking showed that selecting the right classifiers, their
parameters and the meta-classifiers was a critical issue. Most of the research on this topic
hand picks the right combination of classifiers 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 …