A comparative evaluation of combiner and stacked generalization

Combiner and Stacked Generalization are two very similar meta-learningmethods that combine predictions of multiple classiers to improve ac-curacy of any single classier. In this paper, we compare stacked generalization and combiner from the perspective of training eciency versus accuracy. We show that both methods improve the accuracy of any single classier roughly at an equivalent level. Moreover, we also see that the cost of stacked generalization is very large and may prevent it from being used on very large data sets.