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题名: Ensemble Deep Learning for Biomedical Time Series Classification
作者: Jin, LP(金林鹏); Dong, J(董军)
通讯作者: Dong, J(董军)
刊名: COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
发表日期: 2016
DOI: 10.1155/2016/6212684
收录类别: SCI
文章类型: 期刊论文
部门归属: 学科交叉综合研究部
英文摘要: Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.
关键词[WOS]: NEURAL-NETWORK ENSEMBLES ; CLASSIFIERS ; ERROR ; RECOGNITION ; ALGORITHMS ; FORESTS
语种: 英语
JCR小类分区: 四区
WOS记录号: WOS:000385079300001
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sinano.ac.cn/handle/332007/4829
Appears in Collections:学科交叉综合研究部_董军团队_期刊论文

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Recommended Citation:
Jin, LP,Dong, J. Ensemble Deep Learning for Biomedical Time Series Classification[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2016.
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文件名: Ensemble Deep Learning for Biomedical Time Series Classification.pdf
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