Issues in Stacked Generalization

Details Issues in Stacked Generalization Issues in Stacked Generalization

2011-05-27

arxiv.org/abs/1105.5466v1

Journal Of Artificial Intelligence Research, Volume 10, pages 271-289, 1999

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.594

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.

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