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An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease
文献类型：期刊论文
| 作者 | Chen, Hui-Ling1; Wang, Gang2; Ma, Chao3; Cai, Zhen-Nao1,4; Liu, Wen-Bin1; Wang, Su-Jing5,6
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| 刊名 | NEUROCOMPUTING
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| 出版日期 | 2016-04-05 |
| 卷号 | 184期号:0页码:131-144 |
| 关键词 | Kernel extreme learning machine Feature selection Medical diagnosis Parkinson's disease |
| ISSN号 | 0925-2312 |
| 英文摘要 | In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter gamma in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV. (C) 2015 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | FEEDFORWARD NETWORKS ; CLASSIFICATION ; SPEECH ; PERFORMANCE ; ALGORITHMS ; RELEVANCE ; ACCURACY ; ENSEMBLE ; NUMBER |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000374364300014 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/19987]  |
| 专题 | 心理研究所_脑与认知科学国家重点实验室 |
| 作者单位 | 1.Wenzhou Univ, Coll Phys & Elect Informat, Wenzhou 325035, Zhejiang, Peoples R China 2.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China 3.Shenzhen Inst Informat Technol, Sch Digital Media, Shenzhen 518172, Peoples R China 4.Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Peoples R China 5.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China 6.Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Hui-Ling,Wang, Gang,Ma, Chao,et al. An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease[J]. NEUROCOMPUTING,2016,184(0):131-144. |
| APA | Chen, Hui-Ling,Wang, Gang,Ma, Chao,Cai, Zhen-Nao,Liu, Wen-Bin,&Wang, Su-Jing.(2016).An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease.NEUROCOMPUTING,184(0),131-144. |
| MLA | Chen, Hui-Ling,et al."An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease".NEUROCOMPUTING 184.0(2016):131-144. |
入库方式： OAI收割
来源：心理研究所
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