Class-imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class-instances. Data augmentation is a common approach that aims at rebalancing a dataset by injecting more data samples of the minority class instances. In this paper, a new data augmentation approach is proposed using a Generative Adversarial Networks (GAN) to handle the class imbalance problem. Unlike common GAN models, which use a single fake class, the proposed method uses multiple fake classes to ensure a fine-grained generation and classification of the minority class instances. Moreover, the proposed GAN model is conditioned to generate minority class instances aiming at rebalancing the dataset. Extensive experiments were carried out using public datasets, where synthetic samples generated using our model were added to the imbalanced dataset, followed by performing classification using Convolutional Neural Network. Experiment results show that our model can generate diverse minority class instances, even in extreme cases where the number of minority class instances is relatively low. Additionally, superior performance of our model over other common augmentation and oversampling methods was achieved in terms of classification accuracy and quality of the generated samples. (C) 2019 Elsevier B.V. All rights reserved.
