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

TL;DR

This survey reviews two decades of research on AUC maximization, highlighting recent advances in stochastic and deep learning approaches, and discusses future challenges and directions in the field.

Contribution

It provides the first comprehensive overview of AUC maximization literature, including formulations, algorithms, theoretical guarantees, and emerging issues in deep learning.

Findings

Recent surge in deep AUC maximization methods.

Comparison of algorithms and theoretical guarantees.

Identification of future research directions.

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 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 paper 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 deep AUC maximization, and provide suggestions on topics for future work.