On the consistency of auc optimization
WebAUC directly since such direct optimization often leads to NP-hard problem. Instead, surrogate loss functions are usually optimized, such as exponential loss [FISS03, RS09] … 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 ...
On the consistency of auc optimization
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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 … 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.
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. 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 …
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. 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 ...
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
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 … dickens place surgery emailWebIn 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 … dickens place surgery websiteWeb1 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 citizens bank in myrtle beachWebfor 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 … dickens plumbing winter havenWeb10 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 citizens bank in nashville tnWeb25 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 … citizens bank in newport riWeb10 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. citizens bank in new york city