There are several strategies related to imbalanced data in classification.  Resampling techniques, either by oversampling using SMOTE or under sampling to rebalance class distribution.  Algorithm selection: Choose algorithms that work fine on imbalanced datasets; examples are decision trees or ensemble methods.   Performance metrics: Metrics like precision, recall, and the F1 score over accuracy in class imbalance problems.  Data augmentation: Generation of synthetic data to improve the minority class.