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Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions
Yung-Chia Chang
, Kuei Hu Chang
*
, Guan Jhih Wu
*
此作品的通信作者
工業工程與管理學系
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引文 斯高帕斯(Scopus)
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Keyphrases
Financial Institutions
100%
Credit Evaluation
100%
Extreme Gradient Boosting Tree
100%
Classification Accuracy
75%
Imbalanced Data
50%
Multi-stage Modeling
50%
Logistic Regression
25%
Cluster-based
25%
Support Vector Machine
25%
Data Preprocessing
25%
Undersampling
25%
Single-stage
25%
Operative
25%
Self-organizing Algorithm
25%
Credit Risk Models
25%
Assessment Indicators
25%
XGBoost Classifier
25%
XGBoost
25%
Computer Science
Assessment Model
100%
Extreme Gradient Boosting
100%
Gradient Boosting Tree
100%
Classification Accuracy
50%
Imbalanced Data
50%
Logistic Regression
25%
Data Preprocessing
25%
Support Vector Machine
25%
Mathematics
Credit Risk
100%
Boosting Tree
100%
Support Vector Machine
25%
Risk Model
25%
Logistic Regression
25%