Logo image
Back
AUC Maximization in the Era of Big Data and AI: A Survey
Journal article   Open access   Peer reviewed

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

Tianbao Yang and Yiming Ying
ACM computing surveys, Vol.55(8), pp.1-37
12/23/2022
DOI: 10.1145/3554729
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1145/3554729View
Published (Version of record) Open Access

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.
 Big Data ROC deep learning AUC UIOWA OA Agreement

Details

Metrics

20 Record Views
83 Times Cited - Web of Science
Logo image