Generative adversarial network (GAN) is an overwhelming yet promising method to address the data imbalance problem. However, most existing GANs that are usually inspired by computer vision techniques have not yet taken the significance and redundancy of features into consideration delicately, probably producing rough samples with overlapping and incorrectness. To address this problem, a novel GAN called improved GAN with feature filtering (IGAN-FF) is proposed, which establishes a new loss function for the model training by replacing the traditional Euclidean distance with the Mahalanobis distance and taking the ℓ_1,2-norm regularization term into consideration. The remarkable merits of the proposed IGAN-FF can be highlighted as follows: 1) the utilization of the Mahalanobis distance can make a fair evaluation of different attributes without neglecting any trivial/small-scale but significant ones. In addition, it can mitigate the disturbance caused by the correlation between features; 2) the embedding of ℓ_1,2-norm regularization term into the loss function contributes greatly to the feature filtering by guaranteeing the data sparsity as well as helps reduce risk of overfitting. Finally, empirical experiments on 16 well-known imbalanced datasets demonstrate that our proposed IGAN-FF performs better at most evaluation metrics than the other 11 state-of-the-art methods.