  We aim at developing and improving the imbalanced business risk modeling via
jointly using proper evaluation criteria, resampling, cross-validation,
classifier regularization, and ensembling techniques. Area Under the Receiver
Operating Characteristic Curve (AUC of ROC) is used for model comparison based
on 10-fold cross validation. Two undersampling strategies including random
undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two
oversampling methods including random oversampling (ROS) and Synthetic Minority
Oversampling Technique (SMOTE), are applied. Three highly interpretable
classifiers, including logistic regression without regularization (LR),
L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two
ensembling techniques, including Bagging and Boosting, are applied on the DT
classifier for further model improvement. The results show that, Boosting on DT
by using the oversampled data containing 50% positives via SMOTE is the optimal
model and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and
0.8907, respectively.
