# 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 c c} \hline
**Dataset** & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** & **CBUS** \\ \hline Pima & 0.635283 & 0.676792 & 0.680755 & 0.671321 & 0.680189 & 0.73566 & 0.701792 \\ \hline ecoli1 & 0.833333 & 0.941176 & 0.923529 & 0.941176 & 0.892157 & 0.860784 & 0.921569 \\ \hline segment0 & 0.976923 & 0.996203 & 0.976923 & 0.976923 & 0.837975 & 0.984615 & 0.980915 \\ \hline yeast3 & 0.894886 & 0.864583 & 0.885417 & 0.879735 & 0.791667 & 0.893466 & 0.883996 \\ \hline vowel0 & 0.997207 & 0.972067 & 0.997207 & 0.980447 & 0.927374 & 0.969429 & 0.963687 \\ \hline glass2 & 0.5 & 0.717949 & 0.602564 & 0.75641 & 0.717949 & 0.641026 & 0.628205 \\ \hline yeast-1\_vs\_7 & 0.5 & 0.522549 & 0.565686 & 0.547059 & 0.505882 & 0.542157 & 0.921875 \\ \hline ecoli4 & 0.984127 & 0.936508 & 0.984127 & 0.859127 & 0.892157 & 0.984127 & 0.921569 \\ \hline glass-0-1-6\_vs\_5 & 1 & 0.985714 & 1 & 1 & 0.957143 & 1 & 0.957143 \\ \hline page-blocks-1-3\_vs\_4 & 1 & 0.994318 & 1 & 1 & 0.892045 & 1 & 0.982955 \\ \hline yeast-1-4-5-8\_vs\_7 & 0.5 & 0.602273 & 0.507576 & 0.530303 & 0.511364 & 0.55303 & 0.549242 \\ \hline yeast-2\_vs\_8 & 0.5 & 0.684783 & 0.86413 & 0.858696 & 0.875 & 0.706522 & 0.728261 \\ \hline yeast4 & 0.5 & 0.755594 & 0.591259 & 0.768531 & 0.505245 & 0.727273 & 0.795105 \\ \hline winequality-red-4 & 0.5 & 0.734951 & 0.538673 & 0.52411 & 0.569094 & 0.559547 & 0.579935 \\ \hline poker-9\_vs\_7 & 0.5 & 0.808511 & 0.521277 & 0.978723 & 0.829787 & 0.521277 & 0.904255 \\ \hline yeast-1-2-8-9\_vs\_7 & 0.5 & 0.562842 & 0.572404 & 0.56694 & 0.502732 & 0.63388 & 0.618852 \\ \hline winequality-white-9\_vs\_4 & 0.5 & 0.921875 & 1 & 0.546875 & 0.953125 & 0.96875 & 0.921875 \\ \hline yeast5 & 0.934028 & 0.894097 & 0.875 & 0.932292 & 0.947917 & 0.871528 & 0.883681 \\ \hline winequality-red-8\_vs\_6 & 0.5 & 0.542914 & 0.660679 & 0.633733 & 0.680639 & 0.636727 & 0.715569 \\ \hline ecoli-0-1-3-7\_vs\_2-6 & 0.5 & 0.824074 & 0.5 & 0.527778 & 0.509259 & 0.509259 & 0.648148 \\ \hline abalone-21\_vs\_8 & 0.5 & 0.99115 & 0.517699 & 0.986726 & 0.964602 & 0.526549 & 1 \\ \hline yeast6 & 0.5 & 0.895699 & 0.707365 & 0.851953 & 0.50173 & 0.845032 & 0.733564 \\ \hline winequality-white-3\_vs\_7 & 0.5 & 0.823864 & 0.508523 & 0.519886 & 0.732955 & 0.511364 & 0.681818 \\ \hline winequality-red-8\_vs\_6-7 & 0.5 & 0.542914 & 0.660679 & 0.633733 & 0.680639 & 0.636727 & 0.715569 \\ \hline abalone-19\_vs\_10-11-12-13 & 0.5 & 0.628931 & 0.569182 & 0.52673 & 0.611635 & 0.559748 & 0.5 \\ \hline Average AUC Score & 0.65023 & 0.792893 & 0.728426 & 0.759968 & 0.73881 & 0.735139 & 0.793583 \\ \hline Mean Rank & 5.66(7) & 3.42(2) & 3.86(5) & 3.70(4) & 4.34(6) & 3.64(3) & 3.38(1) \\ \hline Friedman test statistics & \multicolumn{5}{c}{Friedman chi-squared = 21.081, df = 6, \(p\)-value = 0.001774} \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of DBT for DT classifier. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** & **CBUS** \\ \hline Pima & 0.694151 & 0.700189 & 0.709623 & 0.728491 & 0.690189 & 0.709623 & 0.690755 \\ \hline ecoli1 & 0.970588 & 0.911765 & 0.921569 & 0.911765 & 0.931373 & 0.921569 & 0.921569 \\ \hline segment0 & 0.980818 & 0.978481 & 0.978286 & 0.981013 & 0.960467 & 0.984615 & 0.991139 \\ \hline yeast3 & 0.849905 & 0.886837 & 0.880682 & 0.886364 & 0.864583 & 0.88447 & 0.882576 \\ \hline vowel0 & 0.941651 & 0.99162 & 0.963842 & 0.921943 & 0.888579 & 0.941651 & 0.96648 \\ \hline glass2 & 0.551282 & 0.717949 & 0.769231 & 0.75641 & 0.705128 & 0.769231 & 0.74359 \\ \hline yeast-1\_vs\_7 & 0.5 & 0.840196 & 0.834314 & 0.840196 & 0.786275 & 0.840196 & 0.78125 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of DBT for LR.