Mitigating class imbalance in multiclass educational data: a hybrid one-vs-one and density-based resampling approach.

Abstract

Class imbalance remains a critical challenge in educational data classification, particularly under multiclass settings where multiple majority and minority categories coexist. These issues are especially detrimental when predicting student performance, as standard machine learning models tend to exhibit bias toward dominant classes, resulting in poor detection of underrepresented, yet academically vulnerable, student groups. This paper presents a novel hybrid resampling framework tailored for multiclass educational datasets characterized by severe imbalance and blurred decision regions. The proposed method integrates one-vs-one decomposition to reduce multiclass complexity, a density-aware undersampling strategy to selectively reduce majority instances within high-density regions, and a targeted minority oversampling using Borderline-SMOTE to enhance decision boundary precision. Empirical evaluations conducted on a proprietary dataset of 3,094 undergraduate health sciences students and four benchmark educational datasets demonstrate the method’s superiority over state-of-the-art resampling techniques. The results validate the framework’s efficacy in improving the generalization and fairness of student performance prediction models in imbalanced multiclass educational settings