Stacking with Multi-response Model Trees

Authors:
Saso Dzeroski;Bernard Zenko
Affiliations:
-;-
Venue:
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Year:
2002

Citing 14
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Abstract

We empirically evaluate several state-of-the-art methods for constructing ensembles of classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. We then propose a new method for stacking, that uses multi-response model trees at the meta-level, and show that it outperforms existing stacking approaches, as well as selecting the best classifier from the ensemble by cross validation.