In feature fusion, should we extract features from the same (seen) data used to train the base models, or from unseen data? Since these features are later used to train a final classifier, I’m concerned that using seen data may introduce bias, overfitting, or even data leakage. What is the best practice to ensure generalization and fairness in this scenario?