  For many real-world classification problems, e.g., sentiment classification,
most existing machine learning methods are biased towards the majority class
when the Imbalance Ratio (IR) is high. To address this problem, we propose a
set convolution (SetConv) operation and an episodic training strategy to
extract a single representative for each class, so that classifiers can later
be trained on a balanced class distribution. We prove that our proposed
algorithm is permutation-invariant despite the order of inputs, and experiments
on multiple large-scale benchmark text datasets show the superiority of our
proposed framework when compared to other SOTA methods.
