CASIA OpenIR  > 机器人理论与应用团队
Rough extreme learning machine: A new classification method based on uncertainty measure
Feng, Lin1; Xu, Shuliang2; Wang, Feilong1; Liu, Shenglan1; Qiao, Hong3,4
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-01-24
Volume325Pages:269-282
Corresponding AuthorFeng, Lin(fenglin@dlut.edu.cn)
AbstractExtreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and a simpler neural network structure on most data sets; RELM cannot only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data. (C) 2018 Elsevier B.V. All rights reserved.
KeywordExtreme learning machine Rough set Attribute reduction Classification Neural network
DOI10.1016/j.neucom.2018.09.062
WOS KeywordARTIFICIAL NEURAL-NETWORK ; HIDDEN NODES ; SET-THEORY ; OPTIMIZATION ; REGRESSION ; SELECTION ; REDUCTS ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61672130] ; National Natural Science Foundation of China[61602082] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; Foundation of LiaoNing Educational Committee[201602151] ; MOE Research Center for Online Education of China[2016YB121] ; Open Program of State Key Laboratory of Software Architecture[SKLSAOP1701] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001]
Funding OrganizationNational Natural Science Foundation of China ; Foundation of LiaoNing Educational Committee ; MOE Research Center for Online Education of China ; Open Program of State Key Laboratory of Software Architecture ; Development of Science and Technology of Guangdong Province Special Fund Project
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000449695000024
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22612
Collection机器人理论与应用团队
Corresponding AuthorFeng, Lin
Affiliation1.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian, Peoples R China
2.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
4.State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Feng, Lin,Xu, Shuliang,Wang, Feilong,et al. Rough extreme learning machine: A new classification method based on uncertainty measure[J]. NEUROCOMPUTING,2019,325:269-282.
APA Feng, Lin,Xu, Shuliang,Wang, Feilong,Liu, Shenglan,&Qiao, Hong.(2019).Rough extreme learning machine: A new classification method based on uncertainty measure.NEUROCOMPUTING,325,269-282.
MLA Feng, Lin,et al."Rough extreme learning machine: A new classification method based on uncertainty measure".NEUROCOMPUTING 325(2019):269-282.
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