title: Ensemble Deep Learning for Biomedical Time Series Classification.
abstract: 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 asBaggingandAdaBoost.
authors.affiliation: Suzhou Institute of Nanotech and Nanobionics, Chinese Academy of Sciences, Suzhou 215123, China; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Lin-Peng
authors.identifier: 0000-0001-7260-8267
authors.initials: LP
authors.lastname: Jin
authors.affiliation: Suzhou Institute of Nanotech and Nanobionics, Chinese Academy of Sciences, Suzhou 215123, China.
authors.forename: Jun
authors.identifier: 0000-0003-4399-8985
authors.initials: J
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D000818:Animals; D035843:Biomedical Research; D002318:Cardiovascular Diseases / Q000145:classification / Q000175:diagnosis; D004562:Electrocardiography; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D013997:Time Factors