A Gaussian-based WGAN-GP oversampling approach for solving the class imbalance problem - Zhou, Qian; Sun, Bo (Zielona Góra: Uniwersytet Zielonogórski)
In practical applications of machine learning, the class distribution of the collected training set is usually imbalanced, i.e., there is a large difference among the sizes of different classes. The class imbalance problem often hinders the achievable generalization performance of most classifier learning algorithms to a large extent. To ameliorate the learning performance, some effective approaches have been proposed in the literature, where the recently presented GAN-based oversampling methods are very representative. However, their generated minority class examples have the risk of high similarity and duplication degree.
Keywords: machine learning; class imbalance; generative adversarial networks; oversampling; data duplication