Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses

doi:10.1016/j.neucom.2017.04.060

科研成果详情

题名	Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses
作者	Wang, Mingjing 1; Chen, Huiling 1,3; Yang, Bo 2,3; Zhao, Xuehua 4; Hu, Lufeng5; Cai, ZhenNao 1; Huang, Hui 1; Tong, Changfei 1
发表日期	2017-12-06
发表期刊	NEUROCOMPUTING 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Kemel extreme learning machine Parameter optimization Feature selection Improved moth-flame optimization Medical diagnosis
其他关键词	PARTICLE SWARM OPTIMIZATION ; FEATURE-SELECTION ; ALGORITHM ; CLASSIFICATION ; MODEL ; PREDICTION
摘要	This study proposes a novel learning scheme for the kernel extreme learning machine (KELM) based on the chaotic moth-flame optimization (CMFO) strategy. In the proposed scheme, CMFO simultaneously performs parameter optimization and feature selection. The proposed methodology is rigorously compared to several other competitive KELM models that are based on the original moth-flame optimization, particle swarm optimization, and genetic algorithms. The comparison is made using the medical diagnosis problems of Parkinson's disease and breast cancer. And the proposed method has successfully been applied to practical medical diagnosis cases. The experimental results demonstrate that, compared to the alternative methods, the proposed method offers significantly better classification performance and also obtains a smaller feature subset. Promisingly, the proposed CMFOFS-KELM, can serve as an effective and efficient computer aided tool for medical diagnosis in the field of medical decision making. (C) 2017 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61303113, 61572226, 61373053]; Science and Technology Plan Project of Wenzhou of China [G20140048, H20110003, Y20160070]; Jilin Province Natural Science Foundation [20150101052JC]; Zhejiang Provincial Natural Science Foundation of ChinaNatural Science Foundation of Zhejiang Province [LY17F020012, LY16H300005, LY14F020035, LQ13G010007]; Guangdong Natural Science FoundationNational Natural Science Foundation of Guangdong Province [2016A030310072]; open project program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172013K01]; Graduate Innovation Fund of Wenzhou University [3162016028]
出版者	ELSEVIER
出版地	AMSTERDAM
ISSN	0925-2312
EISSN	1872-8286
卷号	267 页码:69-84
DOI	10.1016/j.neucom.2017.04.060
页数	16
WOS类目	Computer Science, Artificial Intelligence
WOS研究方向	Computer Science
WOS记录号	WOS:000409285400006
收录类别	SCIE ; EI ; SCOPUS
EI入藏号	20172103690524
EI主题词	Medical problems
URL	查看原文
SCOPUSEID	2-s2.0-85019950299
ESI高被引论文	2020-09 ; 2020-11 ; 2021-01 ; 2021-03 ; 2021-05 ; 2021-07 ; 2021-09 ; 2021-11 ; 2022-01 ; 2022-03 ; 2022-07 ; 2022-09 ; 2022-11 ; 2023-01 ; 2023-03 ; 2023-05 ; 2023-07 ; 2023-09 ; 2023-11 ; 2024-01 ; 2024-03 ; 2024-05 ; 2024-07 ; 2024-09 ; 2024-11
通讯作者地址	[Chen, Huiling]College of Physics and Electronic Information Engineering,Wenzhou University,Wenzhou,325035,China
Scopus学科分类	Computer Science Applications;Cognitive Neuroscience;Artificial Intelligence
TOP期刊	TOP期刊
引用统计	被引频次：339[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/9469
专题	附属第一医院_药学部药学部第一临床医学院（信息与工程学院）、附属第一医院
通讯作者	Chen, Huiling
作者单位	1.College of Physics and Electronic Information Engineering,Wenzhou University,Wenzhou,325035,China; 2.College of Computer Science and Technology,Jilin University,Changchun,130012,China; 3.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun,130012,China; 4.School of Digital Media,Shenzhen Institute of Information Technology,Shenzhen,518172,China; 5.Department of Pharmacy,The First Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China
推荐引用方式 GB/T 7714	Wang, Mingjing,Chen, Huiling,Yang, Bo,et al. Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses[J]. NEUROCOMPUTING,2017,267:69-84.
APA	Wang, Mingjing., Chen, Huiling., Yang, Bo., Zhao, Xuehua., Hu, Lufeng., ... & Tong, Changfei. (2017). Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. NEUROCOMPUTING, 267, 69-84.
MLA	Wang, Mingjing,et al."Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses".NEUROCOMPUTING 267(2017):69-84.