Concerning that traditional single classifiers have poor classification effect for imbalanced data classification, a new binary-class imbalanced data classification algorithm was proposed based on Generative Adversarial Nets (GAN) and ensemble learning, namely Generative Adversarial Nets-Adaptive Boosting-Decision Tree (GAN-AdaBoost-DT). Firstly, GAN training was adopted to get a generative model which produced minority class samples to reduce imbalance ratio. Then, the minority class samples were brought into Adaptive Boosting (AdaBoost) learning framework and their weights were changed to improve AdaBoost model and classification performance of AdaBoost with Decision Tree (DT) as base classifier. Area Under the Carve (AUC) was used to evaluate the performance of classifier when dealing with imbalanced classification problems. The experimental results on credit card fraud data set illustrate that compared with synthetic minority over-sampling ensemble learning method, the accuracy of the proposed algorithm was increased by 4.5%, the AUC of it was improved by 6.5%; compared with modified synthetic minority over-sampling ensemble learning method, the accuracy was increased by 4.9%, the AUC was improved by 5.9%; compared with random under-sampling ensemble learning method, the accuracy was increased by 4.5%, the AUC was improved by 5.4%. The experimental results on other data sets of UCI and KEEL illustrate that the proposed algorithm can improve the accuracy of imbalanced classification and the overall classifier performance.