Open access
2026
Hybrid Approach Based on Supervised Learning and Synthetic Data Generation for University Student Dropout Risk Prediction
The results show that incorporating synthetic data exclusively into the training set improves predictive performance, particularly for minority classes, reflected in gains in precision, recall, and F1-score, while preserving evaluation on untouched real test data.
L. Caicedo, Juan Muñoz, N. Díaz
· International Conference on... · 0 citations