  Without any specific way for imbalance data classification, artificial
intelligence algorithm cannot recognize data from minority classes easily. In
general, modifying the existing algorithm by assuming that the training data is
imbalanced, is the only way to handle imbalance data. However, for a normal
data handling, this way mostly produces a deficient result. In this research,
we propose a class expert generative adversarial network (CE-GAN) as the
solution for imbalance data classification. CE-GAN is a modification in deep
learning algorithm architecture that does not have an assumption that the
training data is imbalance data. Moreover, CE-GAN is designed to identify more
detail about the character of each class before classification step. CE-GAN has
been proved in this research to give a good performance for imbalance data
classification.
