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In a class-imbalanced dataset, since the samples close to the class boundary are more likely to be misclassified, it is of great significance to accurately identify boundary samples for classification. Existing methods usually use K-nearest neighbors to identify boundary samples, but the accuracy needs to be improved. To address the above problem, this paper proposed a synthetic oversampling method for boundary minority samples based on Tomek links. This method first found inter-class samples with the nearest distance to form Tomek links. Then, it identifies those minority samples located at the inter-class boundary according to Tomek links. Next, it used the linear interpolation mechanism in synthetic minority oversampling technology (SMOTE) to perform oversampling between the boundary samples and their minority neighbors, thereby achieving the balance of the datasets. The comparison experiment with eight sampling algorithms shows that the proposed method can obtain higher G-mean and F, values on most of the datasets. [ABSTRACT FROM AUTHOR]

Synthetic minority oversampling technology（SMOTE） is one of the effective methods to solve the classimbalanced problem. However, the linear interpolation mechanism of SMOTE restricts the synthesized samples to the connecting line of the original samples, resulting in a lack of diversity for new samples, and may generate noisy samples when this line passes through the majority class region. In response to the above issues, this paper proposed a generation mechanism for minority samples with hypercuboid constraints. This mechanism constructed a hypercuboid as the generation region of new samples instead of linear interpolation, thereby increasing the variability between the synthesized samples and the original samples. Then, it detected whether there were majority samples in the hypercuboid to determine whether to adjust the hypercuboid, which aimed at preventing the new samples into the region of the majority class. This paper integrated the proposed mechanism into three oversampling methods, i. e., SMOTE, Borderline-SMOTE, and ADASYN, by using it to replace linear interpolation, and then experimentally evaluated the integrated methods on 11 benchmark datasets from KEEL. The results showed that compared to the original methods, the integrated methods could help the classifier to obtain higher F1 and comparable G-mean. It verifies that the hypercuboid generation mechanism can significantly improve the classifier’s ability to recognize minority samples, and meanwhile the majority samples are also taken into account. [ABSTRACT FROM AUTHOR]