LSTMAE-DWSSLM: A unified approach for imbalanced time series data classification-Reference-Cited by-同舟云学术

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LSTMAE-DWSSLM: A unified approach for imbalanced time series data classification

Published:2023-05-10 Issue:18 Volume:53 Page:21077-21091
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Liu Jingjing,Yao Jiepeng,Zhou Qiao,Wang Zhongyi,Huang Lan

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-04642-0.pdf

Reference54 articles.

1. He G, Zhao W, Xia X, Peng R, Wu X (2019) An ensemble of shapelet-based classifiers on inter-class and intra-class imbalanced multivariate time series at the early stage. Soft Comput 23(15):6097–6114. https://doi.org/10.1007/s00500-018-3261-3

2. Lu W, Hou H, Chu J (2018) Feature fusion for imbalanced ECG data analysis. Biomed Signal Process Control 41:152–160. https://doi.org/10.1016/j.bspc.2017.11.010

3. Jiang Z, Zhao W (2021) Fusion algorithm for imbalanced EEG data processing in seizure detection. Seizure 91:207–211. https://doi.org/10.1016/j.seizure.2021.06.023

4. Zhao K, Jiang H, Liu C, Wang Y, Zhu K (2022) A new data generation approach with modified Wasserstein auto-encoder for rotating machinery fault diagnosis with limited fault data. Knowl Based Syst 238:107892. https://doi.org/10.1016/j.knosys.2021.107892

5. Zhang J, Sun Y, Guo L, Gao H, Hong X, Song H (2020) A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeronaut 33(2):439–447. https://doi.org/10.1016/j.cja.2019.07.011

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