# An Oversampling Method for Class Imbalance Problems on Large Datasets
## 4 Experimental Results
### Comparison against State of the Art Oversampling Methods
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **\# Features** & **\# Objects** & **IR** & **Dataset** & **\# Features** & **\# Objects** & **IR** \\ \hline glass1 & 9 & 214 & 1.82 & glass-0-4\_vs\_5 & 9 & 92 & 9.22 \\ ecoli-0\_vs\_1 & 7 & 220 & 1.86 & ecoli-0-3-4-6\_vs\_5 & 7 & 205 & 9.25 \\ wisconsin & 9 & 683 & 1.86 & ecoli-0-3-4-7\_vs\_5-6 & 7 & 257 & 9.28 \\ pima & 8 & 768 & 1.87 & yeast-0-5-6-7\_9\_vs\_4 & 8 & 528 & 9.35 \\ iris0 & 4 & 150 & 2.00 & vowel0 & 13 & 988 & 9.98 \\ glass0 & 9 & 214 & 2.06 & ecoli-0-6-7\_vs\_5 & 6 & 220 & 10.00 \\ yeast1 & 8 & 1484 & 2.46 & glass-0-1-6\_vs\_2 & 9 & 192 & 10.29 \\ haberman & 3 & 306 & 2.78 & ecoli-0-1-4-7\_vs\_2-3-5-6 & 7 & 336 & 10.59 \\ vehicle2 & 18 & 846 & 2.88 & led?digit-0-2-4-5-6-7-8-9\_vs\_1 & 7 & 443 & 10.97 \\ vehicle1 & 18 & 846 & 2.90 & glass-0-6\_vs\_5 & 9 & 108 & 11.00 \\ vehicle3 & 18 & 846 & 2.99 & ecoli-0-1\_vs\_5 & 6 & 240 & 11.00 \\ glass-0-1-2-3\_vs\_4-5-6 & 9 & 214 & 3.20 & glass-0-1-4-6\_vs\_2 & 9 & 205 & 11.06 \\ vehicle0 & 18 & 846 & 3.25 & glass2 & 9 & 214 & 11.59 \\ ecoli1 & 7 & 336 & 3.36 & ecoli-0-1-4-7\_vs\_5-6 & 6 & 332 & 12.28 \\ new-thyroid1 & 5 & 215 & 5.14 & cleveland-0\_vs\_4 & 13 & 177 & 12.62 \\ new-thyroid2 & 5 & 215 & 5.14 & ecoli-0-1-4-6\_vs\_5 & 6 & 280 & 13.00 \\ ecoli2 & 7 & 336 & 5.46 & shuttle-c0-vs\_4 & 9 & 1829 & 13.87 \\ segment0 & 19 & 2308 & 6.02 & yeast-1\_vs\_7 & 7 & 459 & 14.30 \\ glass6 & 9 & 214 & 6.38 & glass4 & 9 & 214 & 15.47 \\ yeast3 & 8 & 1484 & 8.10 & ecoli4 & 7 & 336 & 15.80 \\ ecoli3 & 7 & 336 & 8.60 & page-blocks-1-3\_vs\_4 & 10 & 472 & 15.86 \\ page-blocks0 & 10 & 5472 & 8.79 & abalone9-18 & 8 & 731 & 16.40 \\ ecoli-0-3-4\_vs\_5 & 7 & 200 & 9.00 & glass-0-1-6\_vs\_5 & 9 & 184 & 19.44 \\ yeast-2\_vs\_4 & 8 & 514 & 9.08 & shuttle-c2-vs-cd & 9 & 129 & 20.50 \\ ecoli-0-6-7\_vs\_3-5 & 7 & 222 & 9.09 & yeast-1-4-5-8\_vs\_7 & 8 & 693 & 22.10 \\ ecoli-0-2-3-4\_vs\_5 & 7 & 202 & 9.10 & glass5 & 9 & 214 & 22.78 \\ glass-0-1-5\_vs\_2 & 9 & 172 & 9.12 & yeast-2\_vs\_8 & 8 & 482 & 23.10 \\ yeast-0-3-5-9\_vs\_7-8 & 8 & 506 & 9.12 & yeast4 & 8 & 1484 & 28.10 \\ yeast-0-2-5-7-9\_vs\_3-6-8 & 8 & 1004 & 9.14 & yeast-1-2-8-9\_vs\_7 & 8 & 947 & 30.57 \\ yeast-0-2-5-6\_vs\_3-7-8-9 & 8 & 1004 & 9.14 & yeast5 & 8 & 1484 & 32.73 \\ ecoli-0-4-6\_vs\_5 & 6 & 203 & 9.15 & ecoli-0-1-3-7\_vs\_2-6 & 7 & 281 & 39.14 \\ ecoli-0-1\_vs\_2-3-5 & 7 & 244 & 9.17 & yeast6 & 8 & 1484 & 41.40 \\ ecoli-0-2-6-7\_vs\_3-5 & 7 & 224 & 9.18 & abalone19 & 8 & 4174 & 129.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Datasets from the KEEL repository used in our experiments. Table 4 shows the runtime in seconds to fully balance each KEEL data set with all oversampling methods. An examination of the data presented in the table shows that S-RSB* was the most time-consuming oversampling method. By contrast, the proposed Fast-SMOTE method is clearly the fastest of all the oversampling methods under comparison. The closest method in runtime to our proposed method is NDO, but it was more than twice as time-consuming as the proposed method.