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Rough extreme learning machine: A new classification method based on uncertainty measure

文献类型：期刊论文

作者Feng, Lin1; Xu, Shuliang2; Wang, Feilong1; Liu, Shenglan1; Qiao, Hong3,4
刊名NEUROCOMPUTING
出版日期2019-01-24
卷号325页码:269-282
关键词Extreme learning machine Rough set Attribute reduction Classification Neural network
ISSN号0925-2312
DOI10.1016/j.neucom.2018.09.062
通讯作者Feng, Lin(fenglin@dlut.edu.cn)
英文摘要Extreme 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.
WOS关键词ARTIFICIAL NEURAL-NETWORK ; HIDDEN NODES ; SET-THEORY ; OPTIMIZATION ; REGRESSION ; SELECTION ; REDUCTS ; MODELS
资助项目National 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]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000449695000024
出版者ELSEVIER SCIENCE BV
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/22611]  
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Feng, Lin
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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