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In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions.
F. Wu, J. Zhang, and V. Honavar, “Learning classifiers using hierarchically structured class taxonomies,” in International symposium on abstraction, reformulation, and approximation . Springer, 2005, pp. 313–320
2005
Earlier work this paper cites.
C. X. Ling and V. S. Sheng, “Cost-sensitive learning and the class imbalance problem,” Encyclopedia of machine learning , vol. 2011, pp. 231–235, 2008
2008
Earlier work this paper cites.
S. Puthiya Parambath, N. Usunier, and Y. Grandvalet, “Optimizing f-measures by cost-sensitive classification,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
B. Krawczyk, “Learning from imbalanced data: open challenges and future directions,” Progress in Artificial Intelligence , vol. 5, no. 4, pp. 221–232, 2016
2016
Earlier work this paper cites.
S. S. Mullick, S. Datta, and S. Das, “Generative adversarial minority oversampling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1695–1704
2019
Earlier work this paper cites.
J. Kim, J. Jeong, and J. Shin, “Imbalanced classification via adversarial minority over-sampling,” 2019
2019
Earlier work this paper cites.
Y. Zhou, W. Wang, Z. Qiao, M. Xiao, and Y. Du, “A survey on the construction methods and applications of sci-tech big data knowledge graph,” Sci. Sin. Inf , vol. 50, no. 7, p. 957, 2020
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
M. Xiao, Z. Qiao, Y. Fu, Y. Du, and P. Wang, “Expert knowledge-guided length-variant hierarchical label generation for proposal classification,” 2021 IEEE International Conference on Data Mining , pp. 757–766, 2021
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Z. Qiao, Y. Fu, P. Wang, M. Xiao, Z. Ning, D. Zhang, Y. Du, and Y. Zhou, “Rpt: toward transferable model on heterogeneous researcher data via pre-training,” IEEE Transactions on Big Data , vol. 9, no. 1, pp. 186–199, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Closest in time.
D. Wang, M. Xiao, M. Wu, P. Wang, Y. Zhou, and Y. Fu, “Reinforcement-enhanced autoregressive feature transformation: Gradient-steered search in continuous space for postfix expressions,” 2023
2023
Closest in time.
OpenAI, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
2023
Closest in time.
2023
Closest in time.
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M. Xiao, Z. Qiao, Y. Fu, H. Dong, Y. Du, P. Wang, H. Xiong, and Y. Zhou, “Hierarchical interdisciplinary topic detection model for research proposal classification,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Cited alongside, same era.
X. Ye, M. Xiao, Z. Ning, W. Dai, W. Cui, Y. Du, and Y. Zhou, “Needed: Introducing hierarchical transformer to eye diseases diagnosis,” in Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) . SIAM, 2023, pp. 667–675
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
M. Xiao, D. Wang, M. Wu, Z. Qiao, P. Wang, K. Liu, Y. Zhou, and Y. Fu, “Traceable automatic feature transformation via cascading actor-critic agents,” in Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) . SIAM, 2023, pp. 775–783
2023
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.