# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 4 Results and Discussion
### Performance of DBT for Varying Levels of IR in the Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **01–10** & **11–20** & **21–30** & **31–40** & **41–50** & **Mean** \\ \hline None & 7 & 6 & 7 & 7 & 7 & 6.8 \\ \hline US & 5 & 5 & 5 & 3.5 & 1 & 3.9 \\ \hline OS & 2 & 1 & 6 & 3.5 & 4 & 3.3 \\ \hline HS & 1 & 3 & 4 & 1 & 3 & 2.4 \\ \hline ROSE & 6 & 7 & 3 & 6 & 6 & 5.6 \\ \hline SMOTE & 3 & 2 & 2 & 5 & 5 & 3.4 \\ \hline CBUS & 4 & 4 & 1 & 2 & 2 & 2.6 \\ \hline Kendall’s statistics & & & W = 0.564, chi-sq = 16.9, \(p\)-value = 0.00963 & \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Ranks of DBT for SVM classifier for varying levels of IR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **01–10** & **11–20** & **21–30** & **31–40** & **41–50** & **Mean** \\ \hline None & 7 & 7 & 7 & 7 & 7 & 7 \\ \hline US & 2 & 2 & 3 & 1 & 1 & 1.8 \\ \hline OS & 3.5 & 3.5 & 5.5 & 3 & 3 & 3.7 \\ \hline HS & 5 & 5.5 & 5.5 & 4 & 2 & 4.4 \\ \hline ROSE & 6 & 5.5 & 4 & 6 & 6 & 5.5 \\ \hline SMOTE & 1 & 1 & 2 & 5 & 5 & 2.8 \\ \hline CBUS & 3.5 & 3.5 & 1 & 2 & 4 & 2.8 \\ \hline Kendall’s statistics & & & W = 0.686, chi-sq = 20.6, \(p\)-value = 0.00217 & \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Ranks of DBT for NB classifier for varying levels of IR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **01–10** & **11–20** & **21–30** & **31–40** & **41–50** & **Mean** \\ \hline None & 7 & 7 & 7 & 7 & 7 & 6.8 \\ \hline US & 5 & 5 & 5 & 3.5 & 1 & 3.9 \\ \hline OS & 2 & 1 & 6 & 3.5 & 4 & 3.3 \\ \hline HS & 1 & 3 & 4 & 1 & 3 & 2.4 \\ \hline ROSE & 6 & 7 & 3 & 6 & 6 & 5.6 \\ \hline SMOTE & 3 & 2 & 2 & 5 & 5 & 3.4 \\ \hline CBUS & 4 & 4 & 1 & 2 & 2 & 2.6 \\ \hline Kendall’s statistics & & & W = 0.564, chi-sq = 16.9, \(p\)-value = 0.00963 & \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Ranks of DBT for RF for varying levels of IR. the performance of the data-balancing techniques was not consistent for varying imbalance-ratio percentages. 4. For the NB classifier, there is agreement over the rankings of data-balancing techniques as the \"\(p\)\" value is less than 0.05. However, the concordance value (w) is 0.686, which indicates that there is partial agreement over the rankings. From the ranks of DBT, it is observed that performance of the None was consistent, whereas the performance of other data-balancing techniques was different for varying imbalance-ratio percentages. 5. For the RF classifier, there is agreement over the rankings of data-balancing techniques as the \"\(p\)\" value is less than 0.05. However, the concordance value (w) is 0.539, which indicates that there is partial agreement over the rankings. From the ranks of DBT, it is observed that performance of the None, US, CBUS seemed consistent, whereas the performance of other data-balancing techniques was different for varying imbalance-ratio percentages. 6. For the SVM classifier, there is agreement over the rankings of data-balancing techniques as the \"\(p\)\" value is less than 0.05. However, the concordance value (w) is 0.564, which indicates that there is partial agreement over the rankings. From the ranks of DBT, it is observed that only the performance of the None was consistent, whereas the performance of other data-balancing techniques was different for varying imbalance-ratio percentages. In this section of the paper, we have attempted to answer the following question: Is the performance of the DBT consistent for varying levels of IR in the dataset? The results of the experiment show that for all the classifiers, the performance of the None and ROSE strategy was poor and consistent for varying levels of IR in the dataset. However, performances of the other DBT were not consistent for varying levels of IR in the dataset.