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。

支持向量机的类别不平衡学习方法：回顾与实证评估

Methods for Class-Imbalanced Learning with Support Vector Machines: A  Review and an Empirical Evaluation

Model Weight Averaging (MWA) is a technique that seeks to enhance model's
performance by averaging the weights of multiple trained models. This paper
first empirically finds that 1) the vanilla MWA can benefit the
class-imbalanced learning, and 2) performing model averaging in the early
epochs of training yields a greater performance improvement than doing that in
later epochs. Inspired by these two observations, in this paper we propose a
novel MWA technique for class-imbalanced learning tasks named Iterative Model
Weight Averaging (IMWA). Specifically, IMWA divides the entire training stage
into multiple episodes. Within each episode, multiple models are concurrently
trained from the same initialized model weight, and subsequently averaged into
a singular model. Then, the weight of this average model serves as a fresh
initialization for the ensuing episode, thus establishing an iterative learning
paradigm. Compared to vanilla MWA, IMWA achieves higher performance
improvements with the same computational cost. Moreover, IMWA can further
enhance the performance of those methods employing EMA strategy, demonstrating
that IMWA and EMA can complement each other. Extensive experiments on various
class-imbalanced learning tasks, i.e., class-imbalanced image classification,
semi-supervised class-imbalanced image classification and semi-supervised
object detection tasks showcase the effectiveness of our IMWA.

针对类别不平衡学习任务，在多个训练模型的基础上使用模型权重平均化技术（Model Weight Averaging，MWA）可以提高性能，而在早期时期进行模型平均化比后期效果更好。本文提出了一种名为迭代模型权重平均化（Iterative Model Weight Averaging，IMWA）的用于类别不平衡学习任务的新型 MWA 技术，通过将整个训练阶段划分为多个时期，在每个时期内从相同的初始化模型权重并行训练多个模型，然后进行平均得到一个模型。IMWA 相比于标准 MWA 可以在相同的计算成本下实现更高的性能改进，同时能够进一步提高采用 EMA 策略的方法的性能，从而显示出 IMWA 和 EMA 具有互补效应。对于多个类别不平衡学习任务的大量实验证明了 IMWA 的有效性。

IMWA：迭代模型权重平均在类别不平衡学习任务上的益处

IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced  Learning Tasks

The rapid advancement in data-driven research has increased the demand for
effective graph data analysis. However, real-world data often exhibits class
imbalance, leading to poor performance of machine learning models. To overcome
this challenge, class-imbalanced learning on graphs (CILG) has emerged as a
promising solution that combines the strengths of graph representation learning
and class-imbalanced learning. In recent years, significant progress has been
made in CILG. Anticipating that such a trend will continue, this survey aims to
offer a comprehensive understanding of the current state-of-the-art in CILG and
provide insights for future research directions. Concerning the former, we
introduce the first taxonomy of existing work and its connection to existing
imbalanced learning literature. Concerning the latter, we critically analyze
recent work in CILG and discuss urgent lines of inquiry within the topic.
Moreover, we provide a continuously maintained reading list of papers and code
at this https URL

该论文探讨了图数据分析中类不平衡的学习问题，并提出了结合图表示学习与类不平衡学习的解决方案 - 类不平衡图学习 (CILG)，并介绍了现有工作的分类法和与不平衡学习文献的联系，同时还对 CILG 的最新工作进行了批判性分析和未来研究方向的讨论。