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Large margin distribution multi-class supervised novelty detection

文献类型：期刊论文

作者Zhu, Fa; Zhang, Wenjie4,5; Chen, Xingchi2,6; Gao, Xizhan3; Ye, Ning
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2023-08-15
卷号224页码:119937
ISSN号0957-4174
关键词Novelty detection Multi-class supervised novelty detection Margin distribution Margin theory Large margin distribution-supervised novelty detection
DOI10.1016/j.eswa.2023.119937
文献子类Article
英文摘要As one of state-of-the-art supervised novelty detection models, support vector machine-supervised novelty detection (SVM-SND) can recognize whether a test instance is a novelty or which class the test instance comes from if it is a normal instance through a single model. The SVM-SND inherits the idea of maximizing minimum margin from one-class support vector machine. However, current research has proved that maximizing minimum margin is not the necessary condition to ensure the generalization in classification. This paper introduces margin distribution into multi-class supervised novelty detection and proposes a large margin distribution-supervised novelty detection (LMD-SND) model in which margin distribution is used to enhance the performance for multi-class supervised novelty detection. The margin distribution is represented by margin mean and margin variance. The LMD-SND maximizes the sum of margin means of all classes and minimizes the sum of margin variance of all classes through extra two terms, simultaneously. Compared with SVM-SND, LMD-SND inherits its merits for multi-class supervised novelty detection but has better generalization. The experimental results show that LMD-SND is superior to SVM-SND and can demonstrate comparative performance with shallow multi-class supervised novelty detection models, KNFST and KLMNN, and deep novelty detection models, DSVDD and DMSVDD.
WOS关键词SUPPORT ; ROBUST ; RECOGNITION
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000967057700001
源URL[http://ir.igsnrr.ac.cn/handle/311030/190452]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Univ Jinan, Sch Informat Sci & Engn, Jinan 250024, Peoples R China
2.Pengcheng Lab, Shenzhen 322099, Peoples R China
3.Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110167, Peoples R China
4.Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China
5.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Fa,Zhang, Wenjie,Chen, Xingchi,et al. Large margin distribution multi-class supervised novelty detection[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,224:119937.
APA Zhu, Fa,Zhang, Wenjie,Chen, Xingchi,Gao, Xizhan,&Ye, Ning.(2023).Large margin distribution multi-class supervised novelty detection.EXPERT SYSTEMS WITH APPLICATIONS,224,119937.
MLA Zhu, Fa,et al."Large margin distribution multi-class supervised novelty detection".EXPERT SYSTEMS WITH APPLICATIONS 224(2023):119937.

入库方式： OAI收割

来源：地理科学与资源研究所

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