Employing different optimisation approaches for SMOTE parameter tuning

When performing classification tasks on problems stemming from the real-world phenomena, issues requiring attention often arise due to the specific nature of the data. One common problem is the imbalance of datasets, the resolving of which is performed through several different approaches suggested in the literature. These approaches require the knowledge regarding data distribution and complexity to tune their parameters in order to better deal with the imbalance. This paper aims to give insight on employing various optimisation approaches in order to optimise parameters for the synthetic minority over-sampling technique. Several prominent optimisation approaches are evaluated on different real-world datasets in order to see whether the selection of a specific approach would produce considerably better results thus encouraging the favouring of one approach over the others.