# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 4 Results and Discussion
### Performance of DBT
However, no difference in the performance was observed between None, OS, HS, and ROSE. Further, no significant difference was observed in the performance of US, SMOTE, and CBUS. 5. For the RF classifier, it was found that the performance of the None Strategy was poor and significantly different to the US and CBUS strategies. However, no difference was observed in the performance of the None, OS, HS, ROSE, and SMOTE. Further, no significant difference was observed in the performance of US, HS, SMOTE, and CBUS. 6. For SVM, the performance of the None strategy was poor and significantly different than US, OS, HS, SMOTE, and CBUS. However, no significant difference was observed in the performance of None and ROSE. Further, no significant difference was observed in the performance of US, OS, HS, ROSE, SMOTE, and CBUS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** \\ \hline US & 0.013214 & NA & NA & NA & NA & NA \\ \hline OS & 0.001455 & 0.997137 & NA & NA & NA & NA \\ \hline HS & 0.000148 & 0.924423 & 0.998521 & NA & NA & NA \\ \hline ROSE & 0.33619 & 0.876954 & 0.525494 & 0.217261 & NA & NA \\ \hline SMOTE & 0.003596 & 0.999829 & 0.999988 & 0.989133 & 0.679861 & NA \\ \hline CBUS & 0.000845 & 0.991424 & 1 & 0.999716 & 0.437865 & 0.999829 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Post hoc analysis using Nemenyi multiple comparison test for SVM. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** \\ \hline US & 0.007451 & NA & NA & NA & NA & NA \\ \hline OS & 0.991424 & 0.072553 & NA & NA & NA & NA \\ \hline HS & 0.592448 & 0.547804 & 0.950841 & NA & NA & NA \\ \hline ROSE & 0.999309 & 0.034169 & 0.999974 & 0.862863 & NA & NA \\ \hline SMOTE & 0.862863 & 0.264439 & 0.99792 & 0.999309 & 0.983178 & NA \\ \hline CBUS & 0.003596 & 0.999995 & 0.041588 & 0.416662 & 0.018352 & 0.176038 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Post hoc analysis using Nemenyi multiple comparison test for RF. Therefore, from all the observations above, we can infer that: (i) the performance of the None and ROSE strategies are poor and significantly different than the others; (ii) no significant difference was observed in the performance of US, OS, HS, SMOTE, and CBUS strategies. Dealing with imbalanced datasets is a very common problem in classification tasks, and which DBT is more suitable to enhance the performance of the classifier is the most common question that needs to be answered. In this section, we have attempted to answer this question by applying data-preprocessing-level DBT to 25 different datasets using six different classifiers. From the results of the experiment and statistical analysis, we can infer the following: (i) balancing the imbalanced dataset certainly helps to improve the performance of the classifier; (ii) For DT classifier CBUS and US techniques give a better performance; (iii) For logistic regression SMOTE and OS techniques give a better performance; (iv) For Naive Bayes classifier US and SMOTE give a better performance; (v) For random forest CBUS and US techniques give a better performance; (vi) For support vector machine HS and CBUS give better results; (vii) For kNN classifier OS and SMOTE give better results. However, it is important to note that every time we apply DBT on an imbalanced dataset, there is no guarantee that the same data will be generated or removed in order to balance it. Therefore, model performance and results could also vary slightly.