A Default Prediction Method using XGBoost and LightGBM-Reference-Cited by-同舟云学术

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A Default Prediction Method using XGBoost and LightGBM

Published:2022-10-28 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
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Author:

Zheng Yuntao¹

Affiliation:

1. China Wuhan University,Wuhan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10009640/10009646/10009823.pdf?arnumber=10009823

Reference12 articles.

1. Stability in consumer credit scores: Level and direction of FICO score drift as a precursor to mortgage default and prepayment

2. The strength of weak learnability

3. Lightgbm: A highly efficient gradient boosting decision tree[J];ke;Advances in neural information processing systems,2017

4. Credit Scoring Branches out[J];makowski;Credit World,1985

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving the Accuracy of Oncology Diagnosis: A Machine Learning-Based Approach to Cancer Prediction;International Journal of Online and Biomedical Engineering (iJOE);2024-08-08

2. Ensemble Methodology: Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble;2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI);2024-05-24

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