On the consistency of auc optimization

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August undefined, 2024

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 … Web23 de jun. 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 consistency based on minimizing pairwise surrogate losses. In this paper, we introduce the generalized calibration for AUC optimization, and prove that it is a necessary condition …

AUC optimization and the two-sample problem - NeurIPS

WebAUC optimization on graph data, which is ubiquitous and important, is seldom studied. Different from regular data, AUC optimization on graphs suffers from not only the class imbalance but also topology imbalance. To solve the complicated imbalance problem, we propose a unified topology-aware AUC optimization framework. 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 phinney ridge village

[1208.0645] On the Consistency of AUC Pairwise Optimization - arXiv.org

Web1 de jul. de 2016 · AUC consistency is defined on all measurable functions as in the work of [1], [31], [36]. An interesting problem is to study AUC consistency on linear function spaces for further work. Gao and Zhou [19] gave a sufficient condition and a necessary condition for AUC consistency based on minimizing pairwise surrogate losses, but it … WebWe refer to the method minimizing the PU-AUC risk as PU-AUC optimization. We will theoretically investigate the superiority of RPU in Sect. 4.1. To develop a semi-supervised AUC optimization method later, we also consider AUC optimization form negative and unlabeled data, which can be regarded as a mirror of PU-AUC optimization. tso uniform policy

On the consistency of AUC Optimization - Semantic Scholar

AUC Maximization in the Era of Big Data and AI: A Survey

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 … Web1 de jan. de 2024 · Request PDF On Jan 1, 2024, Zhenhuan Yang and others published Stochastic AUC optimization with general loss Find, read and cite all the research you need on ResearchGate t sound productionWeb5 de dez. de 2016 · It is shown that AUC optimization can be equivalently formulated as a convex-concave saddle point problem and a stochastic online algorithm (SOLAM) is … phinney ridge wellness

"WebTo 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 … " - On the consistency of auc optimization

On the consistency of auc optimization

On the Consistency of AUC Pairwise Optimization

Webranking of the data through empirical AUC maximization. The consistency of the test is proved to hold, as soon as the learning procedure is consistent in the AUC sense and its … 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 …

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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 Optimization In PU learning, we do not have negative data while we can use unlabeled data drawn from marginal density p(x) in addition to positive data: X U:= fxU k g n U k=1 ... 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 …

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. … 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 least square loss which may be not the best option in practice. The main difficulty for dealing with the general convex loss is the pairwise nonlinearity w.r.t. the sampling distribution …

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 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 …

Web6 de dez. de 2024 · Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous …

Web7 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. phinney ridge weatherWeb10 de mai. de 2024 · Area Under the ROC Curve (AUC) is an objective indicator of evaluating classification performance for imbalanced data. In order to deal with large-scale imbalanced streaming data, especially high-dimensional sparse data, this paper proposes a Sparse Stochastic Online AUC Optimization (SSOAO) method. tsounomeWeb18 de jul. de 2024 · Classification: Check Your Understanding (ROC and AUC) Explore the options below. This is the best possible ROC curve, as it ranks all positives above all negatives. It has an AUC of 1.0. In practice, … tso uniform shoesWeb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， … phinney ridge winter shelterWeb2 de ago. de 2012 · AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a … tsounghatWeb3 de ago. de 2012 · Thus, the consistency of AUC is crucial; however, it has been almost untouched before. In this paper, we provide a sufficient condition for the asymptotic consistency of learning approaches based on surrogate loss functions. Based on this result, we prove that exponential loss and logistic loss are consistent with AUC, but … tsoupas albstadtWeb8. 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. … phinney ridge wine bar