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3. Dropout: a simple way to prevent neural networks from overfitting;nitish;The Journal of Machine Learning Research,2014
4. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary;alberto;Journal of Artificial Intelligence Research,2018
5. Random forest vs logistic regression: binary classification for heterogeneous datasets;kaitlin;SMU Data Science Review,2018