What Methods Can Be Used to Address Class Imbalance?
Several methods can be employed to handle class imbalance in epidemiological studies:
1. Resampling Techniques: These include oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class to achieve a balanced dataset.
2. Algorithmic Adjustments: Some machine learning algorithms can be adjusted to account for class imbalance. For example, decision trees can be adjusted to weigh the minority class more heavily.
3. Ensemble Methods: Techniques such as bagging, boosting, and stacking can help improve the performance of predictive models on imbalanced datasets.
4. Anomaly Detection Techniques: When the minority class is extremely rare, anomaly detection methods can be useful. These methods treat the minority class as an anomaly that needs to be detected against a backdrop of normal (majority class) data.
