SMOTE: Synthetic Minority Over-sampling Technique

Understand

An approach to the construction of classifiers from imbalanced datasets is described.

• A dataset is imbalanced if the classification categories are not approximately equally represented.
• Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples.
• It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error.