This paper presents a review on methods for class-imbalanced learning with
the Support Vector Machine (SVM) and its variants. We first explain the
structure of SVM and its variants and discuss their inefficiency in learning
with class-imbalanced data sets. We introduce a hierarchical categorization of
SVM-based models with respect to class-imbalanced learning. Specifically, we
categorize SVM-based models into re-sampling, algorithmic, and fusion methods,
and discuss the principles of the representative models in each category. In
addition, we conduct a series of empirical evaluations to compare the
performances of various representative SVM-based models in each category using
benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our
findings reveal that while algorithmic methods are less time-consuming owing to
no data pre-processing requirements, fusion methods, which combine both
re-sampling and algorithmic approaches, generally perform the best, but with a
higher computational load. A discussion on research gaps and future research
directions is provided.

本论文对于基于支持向量机 (SVM) 及其变种的类不平衡学习方法进行了综述，将 SVM-based 模型按照类不平衡学习进行了层次分类，并比较了各个类别中各种模型的性能，发现融合方法表现最好，但计算负荷较大。对研究空白和未来研究方向进行了讨论。