AUC Maximization in the Era of Big Data and AI: A Survey
AUC Maximization in the Era of Big Data and AI: A Survey
复制标题
DOI：
10.1145/3554729
复制
发表时间：
2023-08-01
影响因子：
16.6
通讯作者：
Ying，Yiming
中科院分区：
文献类型：
--
作者：
Yang，Tianbao;Ying，Yiming
ROC曲线下面积，也称为AUC是用于评估分类器对不平衡数据的性能的选择度量。AUC最大化是指通过直接最大化其AUC得分来学习预测模型的学习范式。它已经被研究了二十多年，可以追溯到90年代后期，从那时起，大量的工作一直致力于AUC最大化。最近，大数据的随机AUC最大化和深度学习的深度AUC最大化（DAM）受到越来越多的关注，并对解决现实世界的问题产生了巨大的影响。然而，据我们所知，没有全面调查的相关工作AUC最大化。本文旨在通过回顾过去二十年的文献来解决差距。我们不仅给出了一个整体的文献，但也提出了详细的解释和比较不同的文件，从配方算法和理论保证。我们还确定和讨论剩余的和新出现的问题，为DAM，并为未来的工作主题提供建议。
Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s, and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization (DAM) for deep learning have received increasing attention and yielded dramatic impact for solving real-world problems. However, to the best our knowledge, there is no comprehensive survey of related works for AUC maximization. This article aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for DAM and provide suggestions on topics for future work.