There are several strategies to tackle imbalanced data in cancer research:
1. Resampling Techniques: Methods like _oversampling_ the minority class or _undersampling_ the majority class can help balance the data. 2. Synthetic Data Generation: Techniques like _SMOTE (Synthetic Minority Over-sampling Technique)_ can create synthetic samples of the minority class to balance the dataset. 3. Algorithmic Approaches: Some algorithms are specifically designed to handle imbalanced data, such as _cost-sensitive learning_ and _ensemble methods_.
