Work

Ensemble Deep Learning for Biomedical Time Series Classification

Year: 2016

Type: article

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.

Source: Computational Intelligence and Neuroscience

DOI: 10.1155/2016/6212684

Authors: Lin-peng Jin, Jun Dong

Institutions: Chinese Academy of Sciences, Suzhou Institute of Nano-tech and Nano-bionics, University of Chinese Academy of Sciences

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

OpenAlex ID: W2522692516