Imbalanced datasets pose a significant challenge in machine learning as they can lead to biased models. One common method to address this is by using techniques like oversampling minority samples, undersampling majority samples, or generating synthetic samples with techniques like SMOTE.

Another approach is to use appropriate evaluation metrics like precision, recall, and F1-score, instead of only relying on accuracy. Additionally, ensemble methods like bagging or boosting can help improve the model's performance by combining multiple classifiers.

You can also consider using cost-sensitive learning algorithms that assign different costs to misclassifications of different classes, which helps in better handling imbalanced data.