ML Anthology

Ensemble Selection from Libraries of Models

Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes
ICML 2004
doi:10.1145/1015330.1015432 /icml/2004/caruana2004icml-ensemble/

Abstract

We present a method for constructing ensembles from libraries of thousands of models. Model libraries are generated using different learning algorithms and parameter settings. Forward stepwise selection is used to add to the ensemble the models that maximize its performance. Ensemble selection allows ensembles to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area. Experiments with seven test problems and ten metrics demonstrate the benefit of ensemble selection.

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Cite

Text

Caruana et al. "Ensemble Selection from Libraries of Models." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015432

Markdown

[Caruana et al. "Ensemble Selection from Libraries of Models." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/caruana2004icml-ensemble/) doi:10.1145/1015330.1015432

BibTeX

@inproceedings{caruana2004icml-ensemble,
  title     = {{Ensemble Selection from Libraries of Models}},
  author    = {Caruana, Rich and Niculescu-Mizil, Alexandru and Crew, Geoff and Ksikes, Alex},
  booktitle = {International Conference on Machine Learning},
  year      = {2004},
  doi       = {10.1145/1015330.1015432},
  url       = {https://mlanthology.org/icml/2004/caruana2004icml-ensemble/}
}