CeC-SMOTE: A Clustering and Centroid-Based Adaptive Oversampling Method for Imbalanced Data

Class imbalance is a common challenge in real-world datasets, leading standard classifiers to perform poorly on underrepresented classes. Traditional oversampling techniques, such as SMOTE and its variants, often generate synthetic samples without fully considering the local data structure, resulting in increased noise and class overlap.This study introduces CeC-SMOTE, an adaptive oversampling method that integrates clustering and centroid-based strategies to enhance the quality of synthetic minority samples. By first partitioning minority instances using K-means clustering, CeC-SMOTE identifies safe and boundary regions, selectively generating new samples where they are most needed while filtering out noise. This targeted approach preserves the underlying distribution of the minority class and minimizes the risk of overfitting. Extensive experiments on artificial and benchmark UCI datasets demonstrate that CeC-SMOTE consistently delivers competitive or superior results compared to established oversampling techniques, particularly in cases with complex or ambiguous class boundaries. Sensitivity analysis confirms that the method is robust to parameter settings, enabling strong performance with minimal tuning.