SetConv: A New Approach for Learning from Imbalanced Data

• URL: http://arxiv.org/abs/2104.06313v1
• Date: Sat, 3 Apr 2021 22:33:30 GMT
• Title: SetConv: A New Approach for Learning from Imbalanced Data
• Authors: Yang Gao, Yi-Fan Li, Yu Lin, Charu Aggarwal, Latifur Khan
• Abstract summary: We propose a set convolution operation and an episodic training strategy to extract a single representative for each class. We prove that our proposed algorithm is permutation-invariant despite the order of inputs.
• Score: 29.366843553056594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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