Abstract—One of the most important aspects of financial
risk is credit risk management. Effective credit rating models
are crucial for the credit institution in assessing credit
applications, they have been widely studied in the field of
statistics and machine learning. Given that small
improvements in credit rating systems can generate significant
profits, any improvement is of high interest to banks and
financial institutions. The ensemble methods are a set of
algorithms whose individual decisions are combined to
perform classification tasks. In this work, we propose an
enhanced experimental comparative study of five ensemble
methods associated with seven base classifiers using six public
credit scoring datasets. Four popular evaluation metrics,
including area under the curve (AUC), accuracy, false positive
rate (FPR) and Time taken to build the model, are employed to
measure the performance of models. The experimental results
and statistical tests show that Pegasos model has a better
overall performance than the other methods analyzed her for
Boosting and Credal Decision Tree (CDT) model has a better
overall performance than the other algorithms in the case of
Bagging, Random Subspace, DECORATE and Rotation Forest.
Index Terms—Credit scoring, ensemble methods, CART, SVMs, Pegasos.
Youssef Tounsi is with Lab. RITM/ESTC Hassan II University Casablanca, Morocco (e-mail: firstname.lastname@example.org).
Cite: Youssef Tounsi, Larbi Hassouni, and Houda Anoun, "An Enhanced Comparative Assessment of Ensemble Learning for Credit Scoring," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 408-415, 2018.