Several techniques can help mitigate the challenges posed by imbalanced data:
1. Resampling Methods: Techniques such as oversampling (increasing the number of minority class instances) and undersampling (reducing the number of majority class instances) can balance the dataset. 2. Synthetic Data Generation: Methods like SMOTE (Synthetic Minority Over-sampling Technique) create synthetic instances of the minority class to balance the dataset. 3. Algorithmic Adjustments: Modifying algorithms to handle imbalanced data, such as using cost-sensitive learning where different misclassification costs are assigned based on the class. 4. Evaluation Metrics: Using appropriate metrics like Precision-Recall curves, F1-score, and ROC curves instead of accuracy to better evaluate model performance on imbalanced datasets.
