中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Optimizing Two-Way Partial AUC With an End-to-End Framework

文献类型：期刊论文

作者Yang, Zhiyong9; Xu, Qianqian1; Bao, Shilong2,3; He, Yuan5; Cao, Xiaochun4; Huang, Qingming6,7,8
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023-08-01
卷号45期号:8页码:10228-10246
关键词AUC Optimization machine learning partial AUC
ISSN号0162-8828
DOI10.1109/TPAMI.2022.3185311
英文摘要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.
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
语种英语
WOS记录号WOS:001022958600063
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/21347]  
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur SKLOIS, Beijing 100093, Peoples R China
3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China
4.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen 518107, Guangdong, Peoples R China
5.Alibaba Grp, Secur Dept, Hangzhou 311121, Zhejiang, Peoples R China
6.Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China
7.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China
8.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
9.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, 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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。