Efficient Generative Adversarial Networks for Imbalanced Traffic Collision Datasets-Reference-Cited by-同舟云学术

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Efficient Generative Adversarial Networks for Imbalanced Traffic Collision Datasets

Published:2022-10 Issue:10 Volume:23 Page:19864-19873
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Chen Mu-Yen¹^ORCID,Chiang Hsiu-Sen²^ORCID,Huang Wei-Kai²

Affiliation:

1. Department of Engineering Science, and the Center for Innovative FinTech Business Models, National Cheng Kung University, Tainan, Taiwan

2. Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan

Funder

Directorate General of Highways, Ministry of Transportation and Communications and the Ministry of Science and Technology, Taiwan

Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9916643/09756847.pdf?arnumber=9756847

Reference50 articles.

1. A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets

2. Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

3. A Neighborhood Undersampling Stacked Ensemble (NUS-SE) in imbalanced classification

4. Improving Identification of Difficult Small Classes by Balancing Class Distribution

5. Asymptotic Properties of Nearest Neighbor Rules Using Edited Data

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