scholarly journals Simultaneous reconstruction of phase and amplitude for wavefront measurements based on nonlinear optimization algorithms

2020 ◽  
Vol 28 (13) ◽  
pp. 19726 ◽  
Author(s):  
Lei Zhao ◽  
Hao Yan ◽  
Jian Bai ◽  
Jing Hou ◽  
Yuhang He ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithms ◽  
Simultaneous Reconstruction

scholarly journals AUC-Maximizing Ensembles through Metalearning

2016 ◽  
Vol 12 (1) ◽  
pp. 203-218 ◽  
Author(s):  
Erin LeDell ◽  
Mark J. van der Laan ◽  
Maya Petersen
Keyword(s):  
Nonlinear Optimization ◽  
Roc Curve ◽  
Optimization Problem ◽  
Binary Classification ◽  
Optimization Algorithms ◽  
Training Data ◽  
Classification Problems ◽  
Nonlinear Optimization Problem ◽  
Auc Maximization ◽  
Super Learner

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.

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