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A Minimax Probability Machine for Nondecomposable Performance Measures

文献类型：期刊论文

作者Luo, Junru4,5; Qiao, Hong1,2; Zhang, Bo3,6,7
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-09-01
页码13
关键词Measurement Task analysis Covariance matrices Support vector machines Prediction algorithms Minimization Kernel Imbalanced classification minimax probability machine nondecomposable performance measures
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3106484
通讯作者Zhang, Bo(b.zhang@amt.ac.cn)
英文摘要Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate (AR), it is usually much more appropriate to use nondecomposable performance measures such as the area under the receiver operating characteristic curve (AUC) and the $F_beta$ measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the AR, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this article is to develop a new minimax probability machine for the $F_beta$ measure, called minimax probability machine for the $F_beta$ -measures (MPMF), which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other nondecomposable performance measures listed in the article. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model.
WOS关键词CLASSIFICATION
资助项目NNSF of China[91948303] ; NNSF of China[61627808]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732226800001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构NNSF of China
源URL[http://ir.ia.ac.cn/handle/173211/46890]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Zhang, Bo
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
4.Changzhou Univ, Aliyun Sch Big Data, Changzhou 213100, Jiangsu, Peoples R China
5.Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou 213100, Jiangsu, Peoples R China
6.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China
7.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Junru,Qiao, Hong,Zhang, Bo. A Minimax Probability Machine for Nondecomposable Performance Measures[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:13.
APA Luo, Junru,Qiao, Hong,&Zhang, Bo.(2021).A Minimax Probability Machine for Nondecomposable Performance Measures.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13.
MLA Luo, Junru,et al."A Minimax Probability Machine for Nondecomposable Performance Measures".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):13.

入库方式： OAI收割

来源：自动化研究所

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