Recent methods from statistics and machine learning for credit scoring
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Summary
While classification trees cannot improve predictive accuracy for the current credit scoring case, the A UC approach and special boosting methods provide outperforming results compared to the robust classical scoring models regarding the predictive performance with the AUC measure.
- Type
- book
- Published
- 2014-05-22
- Cited by
- 14
- References
- 93
- Access
- Open access
- OpenAlex
- https://openalex.org/W4978094
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59737171
Keywords
Benchmark (surveying), Credit risk, Machine learning, Receiver operating characteristic, Computer science
References
- The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation
- C4.5: Programs for Machine Learning (書評)
- Bias Correction in Classification Tree Construction
- Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
- The Importance of Knowing When to Stop
- Regression: Models, Methods and Applications
- Modifying ROC Curves to Incorporate Predicted Probabilities
- A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance
- An Introduction to Recursive Partitioning Using the RPART Routines
- Tree Induction for Probability-Based Ranking
- On Information and Sufficiency
- An Efficient Boosting Algorithm for Combining Preferences
- Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers
- On the Asymptotic Theory of Permutation Statistics
- Boosting Algorithms: Regularization, Prediction and Model Fitting. Comment.
- ada: An R Package for Stochastic Boosting
- Bias in random forest variable importance measures: Illustrations, sources and a solution
- Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance
- Advances in boosting of temporal and spatial models
- On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Cited by
- Parameter estimation for the imbalanced credit scoring data using AUC maximization
- Parameter estimation of linear function using VUS and HUM maximization
- A comparison study of computational methods of Kolmogorov–Smirnov statistic in credit scoring
- Credit Scoring Refinement Using Optimized Logistic Regression
- On the three-way equivalence of AUC in credit scoring with tied scores
- Application of bayesian additive regression trees in the development of credit scoring models in Brazil
- Exploring the potential of machine learning : How machine learning can support financial risk management
- A machine learning approach to credit default prediction and Individual credit scoring
- Application of Machine Learning Algorithms in Credit Card Default Payment Prediction
- Stacked generalizations in imbalanced fraud data sets using resampling methods
- Feature Selection in a Credit Scoring Model
- A novel AUC-based feature selection method: empirical insights from machine learning in the credit scoring problem
- Credit Risk Evaluation Using Cycle Reservoir Neural Networks with Support Vector Machines Readout
- Credit Risk Valuation Using an Efficient Machine Learning Algorithm
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