# 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 \hline
**Dataset** & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** & **CBUS** \\ \hline yeast6 & 0.712556 & 0.852447 & 0.707365 & 0.851953 & 0.583045 & 0.782254 & 0.816115 \\ \hline winequality-white-3\_vs\_7 & 0.619318 & 0.659091 & 0.5 & 0.5 & 0.747159 & 0.502841 & 0.576705 \\ \hline winequality-red-8\_vs\_6-7 & 0.666667 & 0.769461 & 0.663673 & 0.654691 & 0.728543 & 0.651697 & 0.751497 \\ \hline abalone-19\_vs\_10-11-12-13 & 0.5 & 0.514151 & 0.5 & 0.507862 & 0.550314 & 0.514151 & 0.545597 \\ \hline Average AUC Score & 0.694987 & 0.816387 & 0.703387 & 0.743916 & 0.753858 & 0.751677 & 0.790812 \\ \hline Mean Rank & 5.00(7) & 2.84(2) & 4.56(5) & 3.94(3) & 4.72(6) & 4.22(4) & 2.72(1) \\ \hline Friedman test statistics & \multicolumn{6}{c}{Friedman chi-squared = 27.41, df = 6, \(p\)-value = 0.0001213} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** & **CBUS** \\ \hline Pima & 0.675283 & 0.700755 & 0.705189 & 0.691321 & 0.690755 & 0.685755 & 0.681887 \\ \hline ecoli1 & 0.917647 & 0.888235 & 0.911765 & 0.911765 & 0.931373 & 0.911765 & 0.911765 \\ \hline segment0 & 0.992308 & 0.992405 & 0.997468 & 0.997468 & 0.963096 & 0.991042 & 0.994937 \\ \hline yeast3 & 0.846117 & 0.878788 & 0.880682 & 0.882576 & 0.866477 & 0.880682 & 0.878788 \\ \hline vowel0 & 0.883302 & 0.963687 & 0.961049 & 0.97486 & 0.899752 & 0.986034 & 0.96648 \\ \hline glass2 & 0.5 & 0.820513 & 0.769231 & 0.782051 & 0.615385 & 0.769231 & 0.705128 \\ \hline yeast-1\_vs\_7 & 0.5 & 0.644118 & 0.851961 & 0.768627 & 0.780392 & 0.840196 & 0.90625 \\ \hline ecoli4 & 0.992063 & 0.952381 & 0.992063 & 0.992063 & 0.931373 & 0.992063 & 0.992063 \\ \hline glass-0-1-6\_vs\_5 & 1 & 0.857143 & 1 & 0.957143 & 0.928571 & 1 & 0.9 \\ \hline page-blocks-1-3\_vs\_4 & 0.6 & 0.771591 & 0.960227 & 0.982955 & 0.760227 & 0.960227 & 0.823864 \\ \hline yeast-1-4-5-8\_vs\_7 & 0.5 & 0.598485 & 0.579545 & 0.609848 & 0.613636 & 0.587121 & 0.568182 \\ \hline yeast-2\_vs\_8 & 0.875 & 0.875 & 0.701087 & 0.711957 & 0.86413 & 0.695652 & 0.76087 \\ \hline yeast4 & 0.5 & 0.628322 & 0.688811 & 0.695804 & 0.685315 & 0.694056 & 0.733566 \\ \hline winequality-red-4 & 0.5 & 0.68657 & 0.773786 & 0.731877 & 0.710841 & 0.777023 & 0.778479 \\ \hline poker-9\_vs\_7 & 0.5 & 0.819149 & 0.819149 & 0.808511 & 0.819149 & 0.840426 & 0.829787 \\ \hline yeast-1-2-8-9\_vs\_7 & 0.5 & 0.606557 & 0.688525 & 0.637978 & 0.57377 & 0.685792 & 0.60929 \\ \hline winequality-white-9\_vs\_4 & 1 & 0.890625 & 0.5 & 0.984375 & 0.953125 & 0.5 & 0.90625 \\ \hline yeast5 & 0.873264 & 0.951389 & 0.977431 & 0.972222 & 0.970486 & 0.977431 & 0.96875 \\ \hline winequality-red-8\_vs\_6 & 0.5 & 0.763473 & 0.567864 & 0.585828 & 0.540918 & 0.567864 & 0.850299 \\ \hline ecoli-0-1-3-7\_vs\_2-6 & 0.5 & 0.62037 & 0.518519 & 0.537037 & 0.583333 & 0.518519 & 0.601852 \\ \hline abalone-21\_vs\_8 & 0.75 & 0.995575 & 0.986726 & 0.986726 & 0.986726 & 0.986726 & 0.986726 \\ \hline yeast6 & 0.5 & 0.822541 & 0.81562 & 0.81043 & 0.819081 & 0.81735 & 0.80351 \\ \hline winequality-white-3\_vs\_7 & 0.5 & 0.590909 & 0.667614 & 0.661932 & 0.585227 & 0.536932 & 0.627841 \\ \hline winequality-red-8\_vs\_6-7 & 0.5 & 0.763473 & 0.567864 & 0.585828 & 0.540918 & 0.567864 & 0.850299 \\ \hline abalone-19\_vs\_10-11-12-13 & 0.5 & 0.773385 & 0.701258 & 0.70283 & 0.693396 & 0.705975 & 0.831761 \\ \hline Average AUC Score & 0.676199 & 0.794226 & 0.783337 & 0.798561 & 0.772298 & 0.779029 & 0.818745 \\ \hline Mean Rank & 5.90(7) & 3.84(5) & 3.48(3) & 3.16(1) & 4.60(6) & 3.62(4) & 3.40(2) \\ \hline [PERSON] test statistics & \multicolumn{6}{c}{Friedman chi-squared = 30.856, df = 6, \(p\)-value = 2.7 \(\times\) 10\({}^{-5}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of DBT for SVM.