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Feature selection with kernelized multi-class support vector machine
Guo YN(郭一楠)1,4; Zhang ZR(张子睿)1,2; Tang FZ(唐凤珍)2,3
Department机器人学研究室
Source PublicationPattern Recognition
ISSN0031-3203
2021
Volume117Pages:1-13
Indexed BySCI ; EI
EI Accession number20211910320581
WOS IDWOS:000658967900011
Contribution Rank2
Funding OrganizationNatural Science Foundation of Liaoning Province of China (No. 20180520025 ) ; National Natural Science Foundation of China (Grant nos. 61973305 and 61803369 ) ; State Key Laboratory of Robotics (No. 2019-O12 ) ; Innovative Research Groups of the National Natural Science Foundation of China (Grantno. 61821005 )
KeywordFeature selection Multi-class support vector machine Kernel machine Recursive feature elimination
Abstract

Feature selection is an important procedure in machine learning because it can reduce the complexity of the final learning model and simplify the interpretation. In this paper, we propose a novel non-linear feature selection method that targets multi-class classification problems in the framework of support vector machines. The proposed method is achieved using a kernelized multi-class support vector machine with a fast version of recursive feature elimination. The proposed method selects features that work well for all classes, as the involved classifier simultaneously constructs multiple decision functions that separates each class from the others. We formulate the classifier as a large optimisation problem, and iteratively solve one decision function at a time, leading to a lower computational time complexity than when solving the large optimisation problem directly. The coefficients of the classifier are then used as a ranking criterion in the accelerated recursive feature elimination by adding batch elimination and a rechecking process. Experimental results on several datasets demonstrate the superior performance of the proposed feature selection method.

Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS KeywordGENE SELECTION ; SVM-RFE ; CLASSIFICATION
WOS Research AreaComputer Science ; Engineering
Funding ProjectNatural Science Foundation of Liaoning Province of China[20180520025] ; National Natural Science Foundation of China[61973305] ; National Natural Science Foundation of China[61803369] ; State Key Laboratory of Robotics[2019-O12] ; Innovative Research Groups of the National Natural Science Foundation of China[61821005]
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28786
Collection机器人学研究室
Corresponding AuthorTang FZ(唐凤珍)
Affiliation1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114, Nanta Street, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
4.School of Electromechanical and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Recommended Citation
GB/T 7714
Guo YN,Zhang ZR,Tang FZ. Feature selection with kernelized multi-class support vector machine[J]. Pattern Recognition,2021,117:1-13.
APA Guo YN,Zhang ZR,&Tang FZ.(2021).Feature selection with kernelized multi-class support vector machine.Pattern Recognition,117,1-13.
MLA Guo YN,et al."Feature selection with kernelized multi-class support vector machine".Pattern Recognition 117(2021):1-13.
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