Although the experimental results have provided evidence that Safe-Level-SMOTE can be successful classified numeric datasets in the class imbalanced problem, there are several future works left to be studied in this line of research. First, different definitions to assign safe level would be valuable. Second, additional methods to classify datasets which have nominal attributes are useful. Third, automatic determination of the amount of synthetic instances generated by Safe-Level-SMOTE should be addressed