  Training classification models on imbalanced data tends to result in bias
towards the majority class. In this paper, we demonstrate how variable
discretization and cost-sensitive logistic regression help mitigate this bias
on an imbalanced credit scoring dataset, and further show the application of
the variable discretization technique on the data from other domains,
demonstrating its potential as a generic technique for classifying imbalanced
data beyond credit socring. The performance measurements include ROC curves,
Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1
score. The results show that proper variable discretization and cost-sensitive
logistic regression with the best class weights can reduce the model bias
and/or variance. From the perspective of the algorithm, cost-sensitive logistic
regression is beneficial for increasing the value of predictors even if they
are not in their optimized forms while maintaining monotonicity. From the
perspective of predictors, the variable discretization performs better than
cost-sensitive logistic regression, provides more reasonable coefficient
estimates for predictors which have nonlinear relationships against their
empirical logit, and is robust to penalty weights on misclassifications of
events and non-events determined by their apriori proportions.
