A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring

By Yufei Xia, Chuanzhe Liu, Yuying Li, Nana Liu. Expert systems with applications, 2017

723 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/43480aabdc24d2bf8d9e4178bcc391e2bcc7d1bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Jean-Laurent Viviani
2. Hamido Fujita
3. Jie Lang
4. Hui Li
5. Jie Sun

In Artificial Intelligence

1. Abdallah Moubayed
2. Li Yang
3. Abdallah Shami
4. Sami ben Jabeur
5. Jean-Laurent Viviani
6. Salma Mefteh-Wali
7. Changchang Liu
8. Hamido Fujita

In Finance

1. Sami ben Jabeur
2. Jean-Laurent Viviani
3. Salma Mefteh-Wali
4. Hamido Fujita
5. Jie Lang
6. Hui Li
7. Jie Sun

Citations in the corpus

• Forecasting gold price with the XGBoost algorithm and SHAP interaction values (2021, positive): “These findings are in line with the results of Xia et al. (2017) and Climent et al., (2019) who reported the performance of XGBoost compared to traditional models in bankruptcy prediction and credit scoring.”
• An up-to-date comparison of state-of-the-art classification algorithms (2017, result): “In general, our empirical assessment is in accordance with what has been reported in the recent literature ( Ayaki, Yanagimoto, & Yoshioka, 2016; Xia, Liu, Li, & Liu, 2017 ).”
• On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, background): “XGBoost has several additional hyper-parameters, including [60]: ’min child weight’ which means the minimum sum of weights in a child node; ’subsample’ and ’colsample bytree’ used to control the subsampling ratio of instances and”
• MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles (2021, background): “XGBoost [61] is a gradient-boosted decision tree (GBDT) based al-gorithm designed for speed and performance improvement.”
• Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates (2018, methodology): “(2017) propose a novel variant of boosting ensemble learning method with Bayesian hyper-parameter optimization and construct the DT ensemble model for credit scoring, which outperforms baseline models [39].”