On the consistency of auc optimization
Web3 de ago. de 2012 · A sufficient condition for the asymptotic consistency of learning approaches based on surrogate loss functions is provided, and it is proved that … Webis whether the optimization of surrogate losses is consistent with AUC. 1.1. Our Contribution We first introduce the generalized calibration for AUC optimization based on minimizing the pairwise surrogate losses, and find that the generalized cal-ibration is necessary yet insufficient for AUC consistency. For example, hinge
On the consistency of auc optimization
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Webwith AUC, as will be shown by Theorem 1 (Section 4). In contrast, loss functions such as hinge loss are proven to be inconsistent with AUC (Gao & Zhou, 2012). As aforementioned, the classical online setting can-not be applied to one-pass AUC optimization because, even if the optimization problem of Eq. (2) has a closed WebThe Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this …
WebIn this section, we first propose an AUC optimization method from positive and unlabeled data and then extend it to a semi-supervised AUC optimization method. 3.1 PU-AUC … Web25 de jul. de 2015 · To optimize AUC, many learning approaches have been developed, most working with pairwise surrogate losses. Thus, it is important to study the AUC …
Web30 de set. de 2024 · Recently, there is considerable work on developing efficient stochastic optimization algorithms for AUC maximization. However, most of them focus on the … Webfor AUC optimization the focus is mainly on pairwise loss, as the original loss is also defined this way and consistency results for pairwise surrogate losses are available as …
WebHere, consistency (also known as Bayes consistency) guaran-tees the optimization of a surrogate loss will yield an optimal solution with Bayes risk in the limit of infinite sample. …
WebAUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, … shark power lift duo clean vacuum reviewsWeb只有满足一致性，我们才可以替换。高老师的这篇文章On the Consistency of AUC Pairwise Optimization就证明了哪些替代损失函数是满足一致性的。 通过替换不同的损失函数， … shark power pod lift aroundWeb8. One-pass AUC optimization W. Gao, R. Jin, S. Zhu, and Z. Zhou 2013 153 ICML [47] 9. Efficient AUC optimization for classification T. Calders and S. Jaroszewicz 2007 128 PKDD [19] 10. Stochastic online AUC maximization Y. Ying, L. … shark power nozzle replacement partsWeb7 de dez. de 2009 · AUC optimization and the two-sample problem. Pages 360–368. Previous Chapter Next Chapter. ... We show that the learning step of the procedure does not affect the consistency of the test as well as its properties in terms of power, provided the ranking produced is accurate enough in the AUC sense. popular now on bing rewards 210WebTo optimize AUC, many learning approaches have been developed, most working with pairwise surro-gate losses. Thus, it is important to study the AUC consistency based on … popular now on bing rewards 212Webfor AUC optimization the focus is mainly on pairwise loss, as the original loss is also defined this way and consistency results for pairwise surrogate losses are available as well [27]. While these approaches can significantly increase scalability [28], for very large datasets their sequential nature can still be problematic. popular now on bing rewards 2023Web10 de mai. de 2024 · We develop the Data Removal algorithm for AUC optimization (DRAUC), and the basic idea is to adjust the trained model according to the removed data, rather than retrain another model again from ... shark power quality meters