In addition to oversampling, undersampling, and algorithmic approaches, you could also try ensemble methods like bagging or boosting. Bagging can help by training multiple models on different bootstrapped samples of the dataset, while boosting focuses on training models sequentially, giving more attention to misclassified instances. Lastly, if available, you can collect more data for the minority class to alleviate the imbalance.

One approach is to use sampling techniques such as oversampling the minority class or undersampling the majority class. Another strategy is to use algorithms specifically designed for imbalanced datasets, such as SMOTE (Synthetic Minority Over-sampling Technique) or ADASYN (Adaptive Synthetic Sampling). Additionally, performance metrics like precision, recall, and F1 score may provide a better evaluation of model performance for imbalanced datasets.

Another option is to tweak the class weights in the learning algorithm to give more importance to the minority class. Alternatively, you could generate synthetic data using generative models like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs) to balance the dataset. It's also important to cross-validate your models properly to account for the imbalance and avoid overfitting.