Complexity
Volume 2021 (2021), Article ID 9865171, 1 page
https://doi.org/10.1155/2021/9865171

Corrigendum

Corrigendum to “Research on Credit Card Default Prediction Based on -Means SMOTE and BP Neural Network”

Ying Chen and Ruirui Zhang

School of Business, Sichuan Agricultural University, Chengdu 611830, China

Correspondence should be addressed to Ruirui Zhang; zhangruiruisw@163.com

Received 31 May 2021; Accepted 31 May 2021; Published 14 June 2021

Copyright © 2021 Ying Chen and Ruirui Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In the article titled “Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network” [1], the authors would like to clarify that they employed the python package, kmeans-smote 0.1.2, in this study [2]. The error is that a citation to the related article was not included in the original publication, and the following text in Section 3 should be replaced with the addition of the missing references, 22 and 23 [2, 3]:

“Therefore, according to the problem of imbalance of credit card sample categories, this paper uses an improved smote algorithm called k-means SMOTE algorithm” should be replaced with “Therefore, according to the problem of imbalance of credit card sample categories, this paper uses an improved smote algorithm called k-means SMOTE algorithm [22, 23].”

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

Y. Chen and R. Zhang, “Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network,” Complexity, vol. 2021, Article ID 6618841, 13 pages, 2021.

G. Douzas, F. Bacao, and F. Last, “Oversampling for imbalanced learning based on k-means and SMOTE,” 2018, https://arxiv.org/abs/1711.00837.

G. Douzas, F. Bacao, and F. Last, “Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE,” Information Sciences, vol. 465, pp. 1–20, 2018.