On the consistency of auc optimization

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August undefined, 2024

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 well [27]. While these approaches can signiﬁcantly increase scalability [28], for very large datasets their sequential nature can still be problematic. Web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，可以得到不同的目标式，从而进行求解。关于怎么求解AUC的文章也有很多，比如说：

Data Removal from an AUC Optimization Model SpringerLink

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 … Web2 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 … how many days till 22 october

Large-scale Robust Deep AUC Maximization: A New Surrogate …

Web3 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 … 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. 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, … high standard shotgun for sale

Stochastic AUC optimization with general loss

Sparse Stochastic Online AUC Optimization for Imbalanced

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 … 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 … how many days till 23 aprilWebranking 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 capacity to detect ”small” deviations from the homogeneity assumption is illustrated by a simulation example. The rest of the paper is organized as follows. how many days till 23/11/2022

"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

A Unified Framework against Topology and Class Imbalance

Web8. 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. … Web18 de set. de 2024 · Moreover, because of the high complexity of the AUC optimization, many efforts have been devoted to developing efficient algorithms, such as batch and online learnings (Ying, Wen, and Lyu 2016;Gu ...

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Web3 de ago. de 2012 · The purpose of the paper is to explore the connection between multivariate homogeneity tests and AUC optimization, and proposes a two-stage …

Web5 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 … 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 ...

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. … Web10 de mai. de 2024 · We develop an algorithm on Data Removal from an AUC optimization model (DRAUC) and the basic idea is to adjust the trained model using the removed data, ... On the consistency of AUC pairwise optimization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, pp. 939–945 (2015) Google Scholar

Web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， …

Web30 de jul. de 2024 · The Area under the ROC curve (AUC) is a well-known ranking metric for imbalanced learning. The majority of existing AUC-optimization-based machine learning … high standard supermatic 101Web1 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 … how many days till 22 febWeb7 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. high standard sport king pistol disassemblyWebAUC directly since such direct optimization often leads to NP-hard problem. Instead, surrogate loss functions are usually optimized, such as exponential loss [FISS03, RS09] … how many days till 23 novemberWeb28 de mai. de 2024 · Wei Gao and Zhi-Hua Zhou, "On the consistency of AUC pairwise optimization," in International Joint Conference on Artificial Intelligence (IJCAI), 2015. Recommended publications. high standard sport king schematicWeb1 de jul. de 2016 · In this work, we focus on one-pass AUC optimization that requires going through training data only once without having to store the entire training dataset. ... Z. … high standard sports kingWeb30 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 … high standard supermatic citation 104