A Comparative Evaluation of Combiner and Stacked Generalization

Combiner and Stacked Generalization are two very similar meta-learning methods that combine predictions of multiple classiiers to improve accuracy of any single classiier. In this paper, we compare stacked generalization and combiner from the perspective of training eeciency versus accuracy. We show that both methods improve the accuracy of any single classiier 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.