Improving the Recognition Accuracy by Solving the Inherent Data Imbalance Problem of ErrP with Generative Adversarial Network-Reference-Cited by-同舟云学术

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Improving the Recognition Accuracy by Solving the Inherent Data Imbalance Problem of ErrP with Generative Adversarial Network

Published:2023-06-25 Issue: Volume: Page:
ISSN:
Container-title:2023 20th International Conference on Ubiquitous Robots (UR)
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Author:

Jia Yaguang¹,Tao Tangfei²,Xu Guanghua³,Li Min¹,Zhang Sicong¹,Han Chengcheng¹,Wu Qingqiang¹,Pei Jinju¹,Lv Xiaoqing¹,Shi Zhilei¹

Affiliation:

1. School of Mechanical Engineering, Xi'an Jiaotong University,Xi'an,China,710049

2. Xi'an Jiaotong University,Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System,Xi'an,China,710049

3. School of Mechanical Engineering, Xi'an Jiaotong University,State Key Laboratory for Manufacturing Systems Engineering,Xi'an,China,710049

Funder

Key Research and Development Program of Shaanxi province, China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10202211/10202162/10202390.pdf?arnumber=10202390

Reference19 articles.

1. Training cost-sensitive neural networks with methods addressing the class imbalance problem

2. The class imbalance problem: A systematic study1

3. Neural network classification and prior class probabilities;lawrence;Neural Networks Tricks of the Trade,2002

4. Errare machinale est: the use of error-related potentials in brain-machine interfaces

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