Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble

H. Abdi, D. Valentin, and B. Edelman, Neural networks . Sage, 1999, no. 124

1999

Earlier work this paper cites.

T. G. Dietterich, “Ensemble methods in machine learning,” in International workshop on multiple classifier systems . Springer, 2000, pp. 1–15

2000

Earlier work this paper cites.

T. Miyoshi and S. Yamane, “Local ensemble transform kalman filtering with an agcm at a t159/l48 resolution,” Monthly Weather Review , vol. 135, no. 11, pp. 3841–3861, 2007

2007

Earlier work this paper cites.

E. Raffinetti, E. Siletti, and A. Vernizzi, “On the gini coefficient normalization when attributes with negative values are considered,” Statistical Methods & Applications , vol. 24, no. 3, pp. 507–521, 2015

2015

Earlier work this paper cites.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , 2016, pp. 785–794

2016

Earlier work this paper cites.

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

R. Dey and F. M. Salem, “Gate-variants of gated recurrent unit (gru) neural networks,” in 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS) . IEEE, 2017, pp. 1597–1600

2017

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

2017

Cited alongside, same era.

Y. Sayjadah, I. A. T. Hashem, F. Alotaibi, and K. A. Kasmiran, “Credit card default prediction using machine learning techniques,” in 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA) . IEEE, 2018, pp. 1–4

2018

Cited alongside, same era.

M. Soui, S. Smiti, S. Bribech, and I. Gasmi, “Credit card default prediction as a classification problem,” in Recent Trends and Future Technology in Applied Intelligence: 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Montreal, QC, Canada, June 25-28, 2018, Proceedings 31 . Springer, 2018, pp. 88–100

2018

Cited alongside, same era.

S. Yang and H. Zhang, “Comparison of several data mining methods in credit card default prediction,” Intelligent Information Management , vol. 10, no. 5, pp. 115–122, 2018

X. Huang, A. Khetan, M. Cvitkovic, and Z. Karnin, “Tabtransformer: Tabular data modeling using contextual embeddings,” arXiv preprint arXiv:2012.06678 , 2020

Original

2020

Later among the works it cites.

Y. Chen and R. Zhang, “Research on credit card default prediction based on k-means smote and bp neural network,” Complexity , vol. 2021, pp. 1–13, 2021

2021

Later among the works it cites.

J. Gao, W. Sun, and X. Sui, “Research on default prediction for credit card users based on xgboost-lstm model,” Discrete Dynamics in Nature and Society , vol. 2021, pp. 1–13, 2021

2021

Later among the works it cites.

Y. Zheng, “A default prediction method using xgboost and lightgbm,” in 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) . IEEE, 2022, pp. 210–213

2022

Later among the works it cites.