Focused loss-based for imbalanced data scenarios integrated classification methods for CGAN

For the case of imbalanced data, an integrated classification method for CGAN-focal-loss was investigated based on conditional generative adversarial networks (CGAN) using gradient boosting trees. The method first reduces the imbalance rate by CGAN, and further improves the classification performance of the classifier by increasing the focus on a few classes of samples through the weight balancing of the focused loss combined with the GBDT algorithm. The properties of the method were investigated and several theoretical results were obtained. It was proved that the empirical conditional distribution generated by CGAN converges to the conditional distribution of the corresponding aggregate under certain conditions; that the empirical risk of the CGAN method with focused loss converges to the expected risk; and that the estimator of the method converges to the function that minimizes the expected risk. The experimental results show the good performance of the CGAN-focal-loss method.