提出端到端学习框架，让生成数据自动适配模型训练
Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification
- 将数据采样过程设计为可学习的决策组合
- 在多个不平衡数据集上优于当前最优算法
- 适合处理标注不均衡的分类任务
尽管几十年来已有大量研究，类别不平衡仍是机器学习与深度学习模型面临的重大挑战。数据过采样是应对该问题的主要技术，但传统方法常与模型训练阶段脱节，导致表征能力不足。为此，本文提出一种新型学习框架，能够以数据驱动方式生成合成样本。该框架将过采样过程建模为一系列离散决策规则的组合，从而增强模型学习过程的表示能力。在多个不平衡分类任务上的大量实验表明，本框架显著优于现有最先进算法。
原文摘要 · Abstract (English)
Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to address this issue, traditional sampling techniques are often decoupled from the training phase of the predictive model, resulting in suboptimal representations. To address this, we propose a novel learning framework that can generate synthetic data instances in a data-driven manner. The proposed framework formulates the oversampling process as a composition of discrete decision criteria, thereby enhancing the representation power of the model's learning process. Extensive experiments on the imbalanced classification task demonstrate the superiority of our framework over state-of-the-art algorithms.
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