The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.

分类中的数据不平衡问题是一项常见但具有挑战性的任务。在现实世界的数据集中，许多类的分布是不平衡的，在这种情况下的分类结果表明多数数据类存在极大的偏差。最近，人们已经研究了GAN作为少数群体数据的数据增强方法的潜力。在本文中，我们提出了一种分类增强生成对抗网络（CEGAN），以提高生成的合成少数数据的质量，更重要的是，提高数据不平衡条件下的预测准确性。此外，对于多个相似类别降低分类精度的情况，我们提出了一种使用生成的合成少数数据的歧义减少方法。五个基准数据集演示了该方法。