Evaluating Machine Learning Methods for Solving Class Imbalance in Banking Customer Data: A Comparative Study - Boonmunewai, Jirakit; Chantaraksa, Teerapat; Rodjanadid, Benjawan (KKU Science Journal, 2024)
The goal of this research was to address an imbalance problem that affects churn prediction for bank customers. In this study, we examined two sampling techniques Synthetic Minority Over-sampling Technique (SMOTE) and random under-sampling along with three predictive models: the decision tree classifier, Naïve Bayes classifier, and support vector machine classifier. The results indicated that the support vector machine classifier, when combined with SMOTE, was the most effective, achieving a recall of 92.99%, an F-score of 91.37%, an area under the curve (AUC) of 96.4%, and a false negative rate of 7.01%.