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ICMLA
2008

Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features

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Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-features whose values are directly relevant to certain ranges of predictive accuracy for 7 learning algorithms on 135 UCI datasets. Discretization of these 4 meta-features based on thresholds derived from our analysis significantly boosts the accuracy of the meta-level classification task.
Jun Won Lee, Christophe G. Giraud-Carrier
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICMLA
Authors Jun Won Lee, Christophe G. Giraud-Carrier
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