题名 | Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses |
作者 | |
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 |
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 |
发表日期 | 2017-12-06 |
EI入藏号 | 20172103690524 |
EI主题词 | Medical problems |
URL | 查看原文 |
Scopus记录号 | 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 |
引用统计 | |
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 |
通讯作者 | Chen, Huiling |
作者单位 | 1.Wenzhou Univ, Coll Phys & Elect Informat Engn, Wenzhou 325035, Peoples R China; 2.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China; 3.Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China; 4.Shenzhen Inst Informat Technol, Sch Digital Media, Shenzhen 518172, Peoples R China; 5.Wenzhou Med Univ, Affiliated Hosp 1, Dept Pharm, Wenzhou 325000, Peoples R 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. |
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