# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 4 Results and Discussion
### Performance of DBT
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** \\ \hline US & 0.025191 & NA & NA & NA & NA & NA \\ \hline OS & 0.018352 & 1 & NA & NA & NA & NA \\ \hline HS & 0.022697 & 1 & 1 & NA & NA & NA \\ \hline ROSE & 0.281461 & 0.964404 & 0.942875 & 0.958006 & NA & NA \\ \hline SMOTE & 0.000482 & 0.942875 & 0.964404 & 0.950841 & 0.416662 & NA \\ \hline CBUS & 0.102218 & 0.998975 & 0.997137 & 0.998521 & 0.999309 & 0.721504 \\ \hline \end{tabular}
\end{table} 
Table 10: Post hoc analysis using Nemenyi multiple comparison test for LR. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** \\ \hline US & 8.02 \(\times\) 10\({}^{-5}\) & NA & NA & NA & NA \\ \hline OS & 0.140703 & 0.37547 & NA & NA & NA & NA \\ \hline HS & 0.163616 & 0.33619 & 1 & NA & NA & NA \\ \hline ROSE & 0.721504 & 0.034169 & 0.950841 & 0.964404 & NA & NA \\ \hline SMOTE & 0.010548 & 0.902473 & 0.975065 & 0.964404 & 0.481232 & NA \\ \hline CBUS & 0.041588 & 0.679861 & 0.999309 & 0.998521 & 0.761061 & 0.999549 \\ \hline \end{tabular}
\end{table} 
Table 11: Post hoc analysis using Nemenyi multiple comparison test for NB classifier. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** \\ \hline US & 0.004607 & NA & NA & NA & NA & NA \\ \hline OS & 0.050343 & 0.991424 & NA & NA & NA & NA \\ \hline HS & 0.022697 & 0.999309 & 0.999974 & NA & NA & NA \\ \hline ROSE & 0.317359 & 0.741575 & 0.986401 & 0.942875 & NA & NA \\ \hline SMOTE & 0.01647 & 0.999829 & 0.999829 & 1 & 0.913892 & NA \\ \hline CBUS & 0.003596 & 1 & 0.986401 & 0.998521 & 0.700912 & 0.999549 \\ \hline \end{tabular}
\end{table} 
Table 8: Post hoc analysis using Nemenyi multiple comparison test for DT. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** \\ \hline US & 0.004607 & NA & NA & NA & NA & NA \\ \hline OS & 0.050343 & 0.991424 & NA & NA & NA & NA \\ \hline HS & 0.022697 & 0.999309 & 0.999974 & NA & NA & NA \\ \hline ROSE & 0.317359 & 0.741575 & 0.986401 & 0.942875 & NA & NA \\ \hline SMOTE & 0.01647 & 0.999829 & 0.999829 & 1 & 0.913892 & NA \\ \hline CBUS & 0.003596 & 1 & 0.986401 & 0.998521 & 0.700912 & 0.999549 \\ \hline \end{tabular}
\end{table} 
Table 9: Post hoc analysis using Nemenyi multiple comparison test for kNN. The Friedman test statistics show that for all the classifiers, the '\(p^{\prime}\)' value is less than 0.05. So, we can say that, statistically, there is a significance difference in the performance of DBT. As difference in the performance of DBT is significant, the Nemenyi test was then applied to find which DBT has significant difference in the performance. The following are our observations based on the Friedman statistics and Nemenyi post hoc analysis:  
1. For the DT classifier, the performance of the None strategy was poor and significantly different than US, OS, HS, SMOTE, and CBUS. However, no difference in the performance was observed between None and ROSE strategies. Further, no significant difference was observed in the performance of US, OS, HS, SMOTE, and CBUS. 2. For the kNN classifier, the performance of the None strategy was poor and significantly different than OS, HS, ROSE, SMOTE, and CBUS. However, no difference in the performance was observed between the None and US strategy. Further, no significant difference was observed in the performance of OS, HS, ROSE, and CBUS. Significant difference was observed in the performance of US and OS strategies. 3. For the LR classifier, the performance of the None strategy was found to be poor and significantly different than US, OS, HS, and SMOTE. However, no significant difference in the performance was observed between None, ROSE, and CBUS. It was also observed that there was no difference in the performance of US, OS, HS, ROSE, SMOTE, and CBUS. 4. For the NB classifier, the performance of the None strategy was found to be poor and significantly different to US, SMOTE, and CBUS.