PREDICTING CLASS-IMBALANCED BUSINESS RISK USING RESAMPLING, REGULARIZATION, AND MODEL EMSEMBLING ALGORITHMS

PREDICTING CLASS-IMBALANCED BUSINESS RISK USING RESAMPLING, REGULARIZATION, AND MODEL EMSEMBLING ALGORITHMS 

Yan Wang1 , Xuelei Sherry Ni2 

1Graduate College, Kennesaw State University, Kennesaw, USA 2Department of Statistics and Analytical Sciences, Kennesaw State University, Kennesaw, USA 

ABSTRACT 

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. 

KEYWORDS 

Imbalance, resampling, regularization, ensemble, risk modeling

ORIGINAL SOURCE URL : http://aircconline.com/ijmit/V11N1/11119ijmit01.pdf

http://airccse.org/journal/ijmit/vol11.html