# 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 ecoli4 & 0.992063 & 0.835317 & 0.992063 & 0.992063 & 0.931373 & 0.992063 & 0.921569 \\ \hline glass-0-1-6\_vs\_5 & 0.985714 & 0.9 & 0.971429 & 0.542857 & 0.971429 & 0.971429 & 0.871429 \\ \hline page-blocks-1-3\_vs\_4 & 0.6 & 0.977273 & 0.988636 & 0.788636 & 0.777273 & 0.788636 & 0.9375 \\ \hline yeast-1-4-5-8\_vs\_7 & 0.5 & 0.549242 & 0.583333 & 0.632576 & 0.621212 & 0.598485 & 0.560606 \\ \hline yeast-2\_vs\_8 & 0.75 & 0.690217 & 0.804348 & 0.804348 & 0.809783 & 0.804348 & 0.722826 \\ \hline yeast4 & 0.501748 & 0.628322 & 0.685315 & 0.687063 & 0.687063 & 0.694056 & 0.674825 \\ \hline winequality-red-4 & 0.501618 & 0.676861 & 0.77055 & 0.733495 & 0.715696 & 0.777023 & 0.76877 \\ \hline poker-9\_vs\_7 & 0.5 & 0.840426 & 0.861702 & 0.819149 & 0.861702 & 0.87234 & 0.776596 \\ \hline yeast-1-2-8-9\_vs\_7 & 0.5 & 0.780055 & 0.685792 & 0.621585 & 0.637978 & 0.696721 & 0.647541 \\ \hline winequality-white-9\_vs\_4 & 0.5 & 0.90625 & 0.5 & 0.984375 & 0.96875 & 0.5 & 0.78125 \\ \hline yeast5 & 0.871528 & 0.984375 & 0.977431 & 0.911458 & 0.972222 & 0.977431 & 0.982639 \\ \hline winequality-red-8\_vs\_6 & 0.502994 & 0.61477 & 0.567864 & 0.588822 & 0.552894 & 0.576846 & 0.856287 \\ \hline ecoli-0-1-3-7\_vs\_2-6 & 0.518519 & 0.861111 & 0.518519 & 0.537037 & 0.555556 & 0.518519 & 0.583333 \\ \hline abalone-21\_vs\_8 & 1 & 0.960177 & 0.969027 & 0.964602 & 0.973451 & 0.99115 & 0.938053 \\ \hline yeast6 & 0.642857 & 0.791399 & 0.81562 & 0.8087 & 0.81216 & 0.820811 & 0.789669 \\ \hline winequality-white-3\_vs\_7 & 0.5 & 0.698864 & 0.673295 & 0.673295 & 0.801136 & 0.542614 & 0.65625 \\ \hline winequality-red-8\_vs\_6-7 & 0.502994 & 0.61477 & 0.567864 & 0.588822 & 0.552894 & 0.576846 & 0.856287 \\ \hline abalone-19\_vs\_10-11-12-13 & 0.5 & 0.754717 & 0.718553 & 0.72327 & 0.693396 & 0.72327 & 0.783019 \\ \hline Average AUC Score & 0.674337 & 0.803647 & 0.788356 & 0.777133 & 0.788902 & 0.778958 & 0.803432 \\ \hline Mean Rank & 5.64(7) & 3.70(4) & 3.64(2) & 3.68(3) & 4.28(6) & 3.06(1) & 4.00(5) \\ \hline [PERSON] test statistics & \multicolumn{4}{c}{[PERSON] chi-squared = 21.978, df = 6, \(p\)-value = 0.001222} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **None** & **US** & **OS** & **HS** & **ROSE** & **SMOTE** & **CBUS** \\ \hline Pima & 0.693019 & 0.708491 & 0.695189 & 0.637453 & 0.659717 & 0.717925 & 0.718491 \\ \hline ecoli1 & 0.770588 & 0.95098 & 0.917647 & 0.884314 & 0.917647 & 0.884314 & 0.85098 \\ \hline segment0 & 0.97702 & 0.954236 & 0.970691 & 0.975852 & 0.844304 & 0.978286 & 0.954236 \\ \hline yeast3 & 0.529356 & 0.892519 & 0.816761 & 0.836648 & 0.86553 & 0.836174 & 0.859848 \\ \hline vowel0 & 0.938858 & 0.946927 & 0.972067 & 0.960894 & 0.935909 & 0.97486 & 0.969274 \\ \hline glass2 & 0.846154 & 0.717949 & 0.717949 & 0.717949 & 0.717949 & 0.769231 & 0.717949 \\ \hline yeast-1\_vs\_7 & 0.5 & 0.710784 & 0.577451 & 0.571569 & 0.517647 & 0.571569 & 0.859375 \\ \hline ecoli4 & 0.625 & 0.952381 & 0.75 & 0.85119 & 0.917647 & 0.875 & 0.85098 \\ \hline glass-0-1-6\_vs\_5 & 0.514286 & 0.6 & 0.514286 & 0.514286 & 0.571429 & 1 & 0.514286 \\ \hline page-blocks-1-3\_vs\_4 & 0.588636 & 0.9375 & 0.965909 & 0.948864 & 0.682955 & 0.977273 & 0.865909 \\ \hline yeast-1-4-5-8\_vs\_7 & 0.5 & 0.587121 & 0.5 & 0.503788 & 0.503788 & 0.5 & 0.636364 \\ \hline yeast-2\_vs\_8 & 0.5 & 0.63587 & 0.75 & 0.75 & 0.73913 & 0.75 & 0.516304 \\ \hline yeast4 & 0.5 & 0.622028 & 0.629021 & 0.627273 & 0.627273 & 0.627028 & 0.643357 \\ \hline winequality-red-4 & 0.590291 & 0.625243 & 0.593204 & 0.586731 & 0.575405 & 0.605987 & 0.642395 \\ \hline poker-9\_vs\_7 & 0.5 & 0.925532 & 0.510638 & 0.531915 & 0.925532 & 0.914894 & 0.787234 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of DBT for NB classifier.