On combining dynamic selection, sampling, and pool generators for credit scoring

The profitability of the banks highly depends on the models used to decide on the customer's loans. State of the art credit scoring models are based on machine learning methods. These methods need to cope with the problem of imbalanced classes since credit scoring datasets usually contain many paid loans and few not paid ones (defaults). Recently, dynamic selection approaches combined with pre-processing techniques have been evaluated for imbalanced datasets. However, previous works only evaluate oversampling techniques combined with bagging pool generator ensembles. For this reason, we propose to combine different dynamic selection, preprocessing and pool generation techniques. We assess the prediction performance by using four public real-world credit scoring datasets with different levels of imbalanced ratio and four evaluation measures. Experimental results show that KNORA-Union dynamic selection technique combined with Balanced Random Forest improves the classification performance concerning the static ensemble for all levels of imbalance ratio.