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34

ICML
2002
IEEE

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Is Combining Classifiers Better than Selecting the Best One

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Is Combining Classifiers Better than Selecting the Best One

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We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous 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 clearly outperforms existing stacking approaches and selecting the best classifier by cross validation.

Saso Dzeroski, Bernard Zenko

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Cross Validation | Heterogeneous Classifiers | ICML 2002 | Machine Learning |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2002
Where	ICML
Authors	Saso Dzeroski, Bernard Zenko

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