Abstract:
Over the past few years, credit scoring has got an increasing attention for the financial
institutions and has become a popular research topic. The purpose of this study is to
construct an ensemble classification model based on the machine learning techniques
to increase the prediction performance of credit scoring. First; Logistic Regression,
Multivariate Adaptive Regression Splines, Support Vector Machines, Random Forest,
Gradient Boosting are selected as base classifiers and fitted on the data sets. Second,
a stacked generalization ensemble model is integrated through these base classifiers.
The model is evaluated on four real-life credit scoring data sets to test its prediction
performance and effectiveness. Four performance metrics are chosen to evaluate
performance of single base models and stacking. The results demonstrated that the
model has slightly better performance than the single base model classifiers in terms
of different performance criteria. This study shows that this method can be an
alternative and provide an effective decision support for the financial institutions.
