Optimizing Two-Way Partial AUC With an End-to-End Framework

doi:10.1109/TPAMI.2022.3185311

	Optimizing Two-Way Partial AUC With an End-to-End Framework
	Yang, Zhiyong 1; Xu, Qianqian 2; Bao, Shilong 3,4; He, Yuan 5; Cao, Xiaochun 6; Huang, Qingming 7,8,9
	2023-08-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	45 期号:8 页码:10228-10246
摘要	The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we turn to study a more general variant called Two-way Partial AUC (TPAUC), where only the region with TPR >= alpha, FPR <= beta is included in the area. Moreover, a recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics where only the FPR range is restricted, opening a new problem to seek solutions to leverage high TPAUC. Motivated by this, we present the first trial in this article to optimize this new metric. The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, evenwith a proper choice of surrogate loss. To address this issue, we propose a generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning. Moreover, our theoretical analyses show that: 1) the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions, and 2) optimizing the surrogate problems leads to good generalization performance in terms of TPAUC with a high probability. Finally, empirical studies over several benchmark datasets speak to the efficacy of our framework.
关键词	AUC Optimization machine learning partial AUC
DOI	10.1109/TPAMI.2022.3185311
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0102000] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[U1936208] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[62025604] ; National Natural Science Foundation of China[6212200758] ; National Natural Science Foundation of China[61976202] ; Fundamental Research Funds for the Central Universities ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; National Postdoctoral Program for Innovative Talents[BX2021298]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001022958600063
出版者	IEEE COMPUTER SOC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21348
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Qianqian; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, Peoples R China 4.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China 5.Alibaba Grp, Secur Dept, Hangzhou 311121, Zhejiang, Peoples R China 6.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen 518107, Guangdong, Peoples R China 7.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 8.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China 9.Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Yang, Zhiyong,Xu, Qianqian,Bao, Shilong,et al. Optimizing Two-Way Partial AUC With an End-to-End Framework[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(8):10228-10246.
APA	Yang, Zhiyong,Xu, Qianqian,Bao, Shilong,He, Yuan,Cao, Xiaochun,&Huang, Qingming.(2023).Optimizing Two-Way Partial AUC With an End-to-End Framework.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(8),10228-10246.
MLA	Yang, Zhiyong,et al."Optimizing Two-Way Partial AUC With an End-to-End Framework".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.8(2023):10228-10246.