Concretely, sampling methods
are only helpful on two-class tasks, while often cause negative
effect on data sets with big number of classes; threshold-moving
is excellent on two-class tasks, which is capable of
performing cost-sensitive learning even on seriously imbalanced
two-class data sets, and effective on some multi-class
tasks