  Stacked generalization is a general method of using a high-level model to
combine lower-level models to achieve greater predictive accuracy. In this
paper we address two crucial issues which have been considered to be a `black
art' in classification tasks ever since the introduction of stacked
generalization in 1992 by Wolpert: the type of generalizer that is suitable to
derive the higher-level model, and the kind of attributes that should be used
as its input. We find that best results are obtained when the higher-level
model combines the confidence (and not just the predictions) of the lower-level
ones. We demonstrate the effectiveness of stacked generalization for combining
three different types of learning algorithms for classification tasks. We also
compare the performance of stacked generalization with majority vote and
published results of arcing and bagging.
