Several methods are commonly used for feature selection in cancer research:
- Filter Methods: These methods assess the relevance of features based on statistical measures. Common techniques include Pearson correlation, chi-square tests, and mutual information, which evaluate the relationship between each feature and the target variable independently.
- Wrapper Methods: Wrapper methods evaluate feature subsets based on the performance of a specific model. Techniques like Recursive Feature Elimination (RFE) and Forward/Backward Selection are popular. These methods are computationally expensive but often more accurate in identifying useful features.
- Embedded Methods: These methods perform feature selection as part of the model training process. Techniques like LASSO (Least Absolute Shrinkage and Selection Operator) and tree-based methods such as Random Forests and Gradient Boosting Machines are commonly used.
