Although many real-world applications, such as disease prediction, and fault
detection suffer from class imbalance, most existing graph-based classification
methods ignore the skewness of the distribution of classes; therefore, tend to
be biased towards the majority class(es). Conventional methods typically tackle
this problem through the assignment of weights to each one of the class samples
based on a function of their loss, which can lead to over-fitting on outliers.
In this paper, we propose a meta-learning algorithm, named Meta-GCN, for
adaptively learning the example weights by simultaneously minimizing the
unbiased meta-data set loss and optimizing the model weights through the use of
a small unbiased meta-data set. Through experiments, we have shown that
Meta-GCN outperforms state-of-the-art frameworks and other baselines in terms
of accuracy, the area under the receiver operating characteristic (AUC-ROC)
curve, and macro F1-Score for classification tasks on two different datasets.

本论文提出了一种名为 Meta-GCN 的元学习算法，通过同时最小化无偏元数据集损失和优化模型权重来自适应地学习样本权重，实验证明 Meta-GCN 在两个不同数据集上的分类任务中在准确性、接收器操作特性下的曲线下面积（AUC-ROC 曲线）和宏 F1 分数等方面优于现有最先进框架和其他基线模型。