As a whole, border-SMOTE1 behaves excellent on both TP rate and F-value, and
borderline-SMOTE2 behaves super on TP rate because it generates synthetic examples
from both the minority borderline examples and their nearest neighbors of the
majority class, however, the procedure causes overlap between the two classes, thus
decreases its F-value to some extent.