Mitigating the impact of imbalanced data on classifiers is a challenging task in machine learning. Oversampling, a well-known approach, modifies the data distribution and generates minority class samples to alleviate the influence of imbalanced data on classifiers. However, many existing oversampling methods often involve k-nearest neighbors (KNN) techniques, and changes in the KNN parameter values can lead to noticeable instability. The recently proposed Radial-Based Oversampling (RBO) addresses this issue, but it tends to introduce a significant amount of redundant samples and noise after sampling. In this study, we propose a Radial-Based Mixed Sampling method for imbalanced data classification. Firstly, we employ RBO to oversample a portion of the minority class samples. Then, we use the concept of potential to identify redundant samples and noise in the dataset. Finally, Radial-Based Undersampling (RBU) is employed as a data cleaning strategy to eliminate redundant samples and noise from the majority class samples. Extensive comparative experiments are conducted on 10 imbalanced datasets from KEEL, two different classifiers, and five sampling methods. The results indicate that our approach helps classifiers achieve higher F 1 scores and G-means. This suggests a significant improvement in the classifier’s ability to identify minority class samples while considering majority class samples. Additionally, statistical analysis shows higher Friedman Ranking for our method.
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