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• W4226211505 abstract "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." @default.
• W4226211505 created "2022-05-05" @default.
• W4226211505 creator A5023288846 @default.
• W4226211505 creator A5048960543 @default.
• W4226211505 date "2022-12-23" @default.
• W4226211505 modified "2023-10-17" @default.
• W4226211505 title "AUC Maximization in the Era of Big Data and AI: A Survey" @default.
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• W4226211505 doi "https://doi.org/10.1145/3554729" @default.
• W4226211505 hasPublicationYear "2022" @default.
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