论文标题
通过增强数据通过明确梯度学习不平衡分类
Imbalanced Classification via Explicit Gradient Learning From Augmented Data
论文作者
论文摘要
从不平衡数据中学习是现实世界分类任务中最重要的挑战之一。在这种情况下，由于偏爱多数级别，神经网络的性能会大大损害。现有方法试图通过重新采样或重新加权学习过程中的损失来消除偏见。尽管如此，这些方法倾向于过度拟合少数族裔样本，并且当少数群体的结构高度不规则时表现不佳。在这里，我们提出了一种新颖的深度元学习技术，以增强具有新的少数民族实例的给定不平衡数据集。这些附加数据纳入了分类器的深度学习过程中，并明确地学习了它们的贡献。该方法的优势在具有不同不平衡比率的合成和现实数据集中证明。
Learning from imbalanced data is one of the most significant challenges in real-world classification tasks. In such cases, neural networks performance is substantially impaired due to preference towards the majority class. Existing approaches attempt to eliminate the bias through data re-sampling or re-weighting the loss in the learning process. Still, these methods tend to overfit the minority samples and perform poorly when the structure of the minority class is highly irregular. Here, we propose a novel deep meta-learning technique to augment a given imbalanced dataset with new minority instances. These additional data are incorporated in the classifier's deep-learning process, and their contributions are learned explicitly. The advantage of the proposed method is demonstrated on synthetic and real-world datasets with various imbalance ratios.
