Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC
Not on the map. Why?
Abstract (arXiv)
In this paper, we show the arc length of the optimal ROC curve is an f-divergence. By leveraging this result, we express the arc length using a variational objective and estimate it accurately using positive and negative samples. We show this estimator has a non-parametric convergence rate Op(n-β/4) (β ∈ (0,1] depends on the smoothness). Using the same technique, we show the surface area between the optimal ROC curve and the diagonal can be expressed via a similar variational objective. These new insights lead to a novel classification procedure that maximizes an approximate lower bound of the maximal AUC. Experiments on CIFAR-10 datasets show the proposed two-step procedure achieves good AUC performance in imbalanced binary classification tasks.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 47 references that are in the map. First posted on arXiv 18 Oct 2021.
Builds on
7 of 47 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Variational Inference: A Review for Statisticianscited by 1,466
- 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
- 2017PacGAN: The power of two samples in generative adversarial networkscited by 104
- 2013One-Pass AUC Optimizationcited by 42
- 2013Density Estimation in Infinite Dimensional Exponential Familiescited by 41
- 2019Stochastic Proximal AUC Maximizationcited by 14
Built on by
Not cited yet.