An adaptive Bagging algorithm based on lightweight transformer for multi-class imbalance recognition-Reference-Cited by-同舟云学术

An adaptive Bagging algorithm based on lightweight transformer for multi-class imbalance recognition

Published:2024-03-28 Issue:2 Volume:30 Page:
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Wang Junyi,Jiang Xuezheng,Liu Hailian,Cai Haibin,Meng Qinggang

Funder

National Natural Science Foundation of China

Chunhui Plan Cooperative Project of Ministry of Education under Grant

Basic and Applied Basic esearch Foundation of Guangdong Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00530-024-01299-8.pdf

Reference37 articles.

1. Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J Big Data. 6(1), 27 (2019). https://doi.org/10.1186/s40537-019-0192-5

2. Zheng, X., Han, B., Ni, Z.: Tourism route recommendation based on a multi-objective evolutionary algorithm using two-stage decomposition and pareto layering. IEEE-CAA J. Autom. Sin. 10(2), 486–500 (2023). https://doi.org/10.1109/jas.2023.123219

3. Yang, Y., Zha, K., Chen, Y., Wang, H., Katabi, D.: Delving into deep imbalanced regression. In: Proc. Int. Conf. Machin. Learn. (ICML), pp. 11842–11851 (2021)

4. Azad, C., Bhushan, B., Sharma, R., Shankar, A., Singh, K.K., Khamparia, A.: Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus. Multimed. Syst. 28, 1–19 (2022). https://doi.org/10.1007/s00530-021-00817-2

5. Chao, X., Zhang, L.: Few-shot imbalanced classification based on data augmentation. Multimed. Syst. 29, 2843–2851 (2023). https://doi.org/10.1007/s00530-021-00827-0