  Class imbalance refers to the significant difference in the number of samples
from different classes within a dataset, making it challenging to identify
minority class samples correctly. This issue is prevalent in real-world
classification tasks, such as software defect prediction, medical diagnosis,
and fraud detection. The synthetic minority oversampling technique (SMOTE) is
widely used to address class imbalance issue, which is based on interpolation
between randomly selected minority class samples and their neighbors. However,
traditional SMOTE and most of its variants only interpolate between existing
samples, which may be affected by noise samples in some cases and synthesize
samples that lack diversity. To overcome these shortcomings, this paper
proposes the Minimum Enclosing Ball SMOTE (MEB-SMOTE) method from a geometry
perspective. Specifically, MEB is innovatively introduced into the oversampling
method to construct a representative point. Then, high-quality samples are
synthesized by interpolation between this representative point and the existing
samples. The rationale behind constructing a representative point is discussed,
demonstrating that the center of MEB is more suitable as the representative
point. To exhibit the superiority of MEB-SMOTE, experiments are conducted on 15
real-world imbalanced datasets. The results indicate that MEB-SMOTE can
effectively improve the classification performance on imbalanced datasets.
