Ensemble Deep Learning for Biomedical Time Series Classification
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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.
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The ensemble model is a method to improve the accuracy rate of the model.
“The ensemble model is a method to improve the accuracy rate of the model [26, 27].”
From Method for diagnosis of acute lymphoblastic leukemia based on vit-cnn ensemble model (2021) · cites this paper as [27]
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Cited by
Sorted by citations
Method for diagnosis of acute lymphoblastic leukemia based on vit-cnn ensemble model
Jiang, Z., Dong, Z., Wang, L., & Jiang, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/7529893 · Cited by 124 · see citing works
Deep Neural Network Ensembles for Time Series Classification
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- Authors
- Jin, L.-P., & Dong, J.
- Affiliations
- Suzhou Institute of Nanotech and Nanobionics, Chinese Academy of Sciences, Suzhou 215123, China
- Chinese Academy of Sciences (CN)
- Corresponding author
- Dong, Jun
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2016
- Volume
- 2016
- Pages
- 1-13
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- PMC
- PMC5048093
- OpenAlex
- W2522692516
- Cited by
- 60
- References
- 53
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 3 · 2022: 10 · 2021: 14 · 2020: 5 · 2019: 6 · 2018: 7 · 2017: 3
- FWCI
- 4.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- ensemble deep learning, electrocardiogram, cardiovascular disease, view integration, local views, simple average
- MeSH terms
- Algorithms, Animals, Cardiovascular Diseases, Electrocardiography, Humans, Learning, Predictive Value of Tests, Time Factors, Neural Networks, Computer, Biomedical Research
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