Apart from the aforementioned techniques, one could also explore using anomaly detection algorithms, such as One-Class SVM or the Isolation Forest, to detect instances belonging to the minority class. Another approach is to use active learning, where the model is trained on the partially labeled dataset initially and then learns from the hardest-to-classify instances, thus focusing on improving the accuracy of the minority class.

In addition to undersampling and oversampling, another technique to handle imbalanced datasets is to use cost-sensitive learning. This approach involves assigning higher misclassification costs to the minority class, thereby encouraging the model to pay more attention to correctly classifying the minority class instances. Another strategy is to utilize ensemble methods, such as Random Forests or Gradient Boosting, which are known to perform well on imbalanced datasets by combining multiple weak classifiers to form a strong classifier.

One approach to handle imbalanced datasets is to use techniques like undersampling or oversampling. Undersampling involves randomly removing samples from the majority class, while oversampling involves creating additional synthetic samples for the minority class. Another approach is to use algorithms that are specifically designed to handle imbalanced data, such as the SMOTE (Synthetic Minority Over-sampling Technique) algorithm, which generates synthetic samples for the minority class based on its nearest neighbors.