Charu Aggarwal


2020

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SetConv: A New Approach for Learning from Imbalanced Data
Yang Gao | Yi-Fan Li | Yu Lin | Charu Aggarwal | Latifur Khan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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.