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W2405366961 abstract "Abstract Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree." @default.
W2405366961 created "2016-06-24" @default.
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W2405366961 date "2016-05-01" @default.
W2405366961 modified "2023-10-17" @default.
W2405366961 title "AUC-Maximizing Ensembles through Metalearning" @default.
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W2405366961 doi "https://doi.org/10.1515/ijb-2015-0035" @default.
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