Ensemble Deep Learning for Biomedical Time Series Classification

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...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2016-01, Vol.2016 (2016), p.204-216
Main Authors: Jin, Lin-peng, Dong, Jun
Format: Article
Language:English
Subjects:
Algorithms
Animals
Artificial intelligence
Biomedical Research
Cardiovascular Diseases - classification
Cardiovascular Diseases - diagnosis
Classification
Decision making
Deep learning
Distortion
Electrocardiography
Filtering
Humans
Learning - physiology
Medical research
Medicine, Experimental
Methods
Neural networks
Neural Networks (Computer)
Pattern recognition
Predictive Value of Tests
Recording
Technology application
Time Factors
Time series
Time-series analysis
Training
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/6212684