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Development and evaluation of a deep learning-assisted diagnostic support system for radiographer preliminary clinical evaluation of intracranial hemorrhage. [PDF]
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Maximization of AUC and Buffered AUC in binary classification
Mathematical Programming, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stan Uryasev, Uryasev Stan
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Proximal Stochastic AUC Maximization
2020 International Joint Conference on Neural Networks (IJCNN), 2020This work considers a stochastic optimization problem for maximizing the AUC (area under the ROC curve). The AUC metric has proven to be a reliable performance measure for evaluating a model learned on imbalanced data. The batch pairwise learning methods (e.g., rankSVM) can achieve a quadratic convergence to the optimal solution.
Majdi Khalid +2 more
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International Journal of Pattern Recognition and Artificial Intelligence, 2010
AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample pairs playing the same roles as the support vector samples in SVM. Such a learning paradigm generally emphasizes more on the local discriminative information just associated ...
Yunyun Wang, Songcan Chen, Hui Xue 0002
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AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample pairs playing the same roles as the support vector samples in SVM. Such a learning paradigm generally emphasizes more on the local discriminative information just associated ...
Yunyun Wang, Songcan Chen, Hui Xue 0002
openaire +1 more source
Twenty-first international conference on Machine learning - ICML '04, 2004
We present a statistical analysis of the AUC as an evaluation criterion for classification scoring models. First, we consider significance tests for the difference between AUC scores of two algorithms on the same test set. We derive exact moments under simplifying assumptions and use them to examine approximate practical methods from the literature. We
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We present a statistical analysis of the AUC as an evaluation criterion for classification scoring models. First, we consider significance tests for the difference between AUC scores of two algorithms on the same test set. We derive exact moments under simplifying assumptions and use them to examine approximate practical methods from the literature. We
openaire +1 more source