Enhancing Element Game Classification: Effective Techniques for Handling Imbalanced Classes

Abstract

One strategy for enhancing the promotion of marketing content involves the incorporation of gamification techniques. Existing research that examines the utilization of gamification through the classification approach often overlooks the issue of class imbalance within the dataset. Class imbalance occurs when the number of instances in the minority class differs significantly from those in the dominant class. This discrepancy can cause the classification method to perform worse because most classifiers work best when the class distribution in the dataset is balanced. Multiple studies underscore the significance of addressing class imbalances as a pivotal step toward enhancing the performance of classification algorithms. This study aims to demonstrate the impact of the data imbalance challenge and to identify the superior resampling method for integration into the machine learning process. The dataset employed pertains to users of a mobile banking application in Indonesia and encompasses variables such as gamification elements, demographic information, psychological factors, and customer engagement metrics. The chosen resampling technique for this study is SMOTE-Tomek. The various classification methods utilized include linear regression, K-NN, CART, random forest, SVM, stacking ensemble, and XGBoost. Among these methods, the SMOTE-Tomek resampling approach proves most effective when combined with the stacking ensemble classification. This combination yields a remarkable accuracy rate of 98.7% during 10-fold cross-validation, along with an impressive geometric mean score of 0.99. The thorough evaluation findings demonstrated that the approach presented in this study has a promising practical applicability and effectively identifies unbalanced elementary games.