Oversampling method using outlier detectable generative adversarial network

A class imbalance problem occurs when a particular class of data is significantly more or less than another class of data. This problem is difficult to solve; however, solutions such as the oversampling method using synthetic minority oversampling technique (SMOTE) or conditional generative adversarial network (cGAN) have been suggested recently to solve this problem. In the case of SMOTE and their variations, it is possible to generate biased artificial data because it does not consider the entire data in the minority class. To overcome this problem, an oversampling method using cGAN has been proposed. However, such a method does not consider the majority class that affects the classification boundary. In particular, if there is an outlier in the majority class, the classification boundary may be biased. This paper presents an oversampling method using outlier detectable generative adversarial network (OD-GAN) to solve this problem. We use a discriminator, which is used only for training purposes in cGAN, as an outlier detector to quantify the difference between the distributions of the majority and minority classes. The discriminator can detect and remove outliers. This prevents the distortion of the classification boundary caused by outliers. The generator imitates the distribution of the minority class and generates artificial data to balance the dataset. We experiment with various datasets, oversampling techniques, and classifiers. The empirical results show that the performance of OD-GAN is better than those of other oversampling methods for imbalanced datasets with outliers.