Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
This provides a robust solution for handling imbalanced datasets in classification tasks, though it is incremental as it builds upon existing SMOTE methods.
The paper tackles the problem of imbalanced classification by enhancing the SMOTE algorithm with convex combination and kernel-based weighting to generate better synthetic minority class samples, resulting in improved F1-score, G-mean, and AUC on multiple real-world datasets.
This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination and kernel-based weighting to generate synthetic samples that better represent the minority class. Through experiments on multiple real-world datasets, we demonstrate that the new technique outperforms existing methods in terms of F1-score, G-mean, and AUC, providing a robust solution for handling imbalanced datasets in classification tasks.