# 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 c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{10}{c}{**Oversampling Method**} \\ \cline{2-13}  & **SMOTE** & **Borderline** & **SafeLevel** & **LNE** & **NDO** & **S-RSB* & **K-Means-S** & **Farthest-S** & **G-SMOTE** & **S-SF** & **Fast-SMOTE** \\ \hline glass-04\_vs\_5 & 0.0027 & 0.0006 & 0.0056 & 0.0040 & 0.0002 & 0.0045 & 0.0035 & 0.0025 & 0.0037 & 0.0003 & 0.0001 \\ glass-06\_vs\_5 & 0.0033 & 0.0007 & 0.0069 & 0.0048 & 0.0003 & 0.0054 & 0.0040 & 0.0031 & 0.0044 & 0.0002 & 0.0001 \\ shuttle-c2\_vs\_5+4 & 0.0041 & 0.0004 & 0.0094 & 0.0058 & 0.0004 & 0.0067 & 0.0052 & 0.0040 & 0.0062 & 0.0003 & 0.0002 \\ iris0 & 0.0017 & 0.0016 & 0.0039 & 0.0038 & 0.0002 & 0.0030 & 0.0028 & 0.0017 & 0.0024 & 0.0011 & 0.0001 \\ glass-01-5\_vs\_2 & 0.0052 & 0.0011 & 0.0113 & 0.0076 & 0.0004 & 0.0083 & 0.0068 & 0.0049 & 0.0070 & 0.0004 & 0.0002 \\ Cleveland\_0\_vs\_4 & 0.0054 & 0.0009 & 0.0124 & 0.0081 & 0.0005 & 0.0096 & 0.0078 & 0.0051 & 0.0072 & 0.0004 & 0.0002 \\ glass-01-6\_vs\_5 & 0.0059 & 0.0007 & 0.0136 & 0.0083 & 0.0005 & 0.0101 & 0.0069 & 0.0057 & 0.0080 & 0.0004 & 0.0002 \\ glass-01-6\_vs\_2 & 0.0056 & 0.0010 & 0.0132 & 0.0084 & 0.0005 & 0.0102 & 0.0070 & 0.0057 & 0.0076 & 0.0005 & 0.0002 \\ recoil-03-4\_vs\_5 & 0.0062 & 0.0009 & 0.0132 & 0.0083 & 0.0005 & 0.0091 & 0.0086 & 0.0055 & 0.0076 & 0.0005 & 0.0002 \\ recoil-02-3-4\_vs\_5 & 0.0062 & 0.0009 & 0.0136 & 0.0092 & 0.0005 & 0.0097 & 0.0088 & 0.0055 & 0.0082 & 0.0005 & 0.0002 \\ recoil-04-6\_vs\_5 & 0.0060 & 0.0009 & 0.0132 & 0.0091 & 0.0005 & 0.0091 & 0.0079 & 0.0055 & 0.0080 & 0.0005 & 0.0002 \\ signal-03-4\_6\_vs\_5 & 0.0061 & 0.0009 & 0.0136 & 0.0091 & 0.0005 & 0.0093 & 0.0081 & 0.0057 & 0.0082 & 0.0005 & 0.0002 \\ glass-01-4\_6\_vs\_2 & 0.0066 & 0.0012 & 0.0144 & 0.0092 & 0.0005 & 0.0107 & 0.0082 & 0.0062 & 0.0088 & 0.0004 & 0.0002 \\ glass1 & 0.0023 & 0.0032 & 0.0052 & 0.0062 & 0.0002 & 0.0061 & 0.0035 & 0.0022 & 0.0031 & 0.0026 & 0.0001 \\ glass2 & 0.0065 & 0.0010 & 0.0152 & 0.0099 & 0.0005 & 0.0113 & 0.0087 & 0.0065 & 0.0087 & 0.0005 & 0.0003 \\ glass80 & 0.0026 & 0.0031 & 0.0059 & 0.0092 & 0.0002 & 0.0068 & 0.0038 & 0.0026 & 0.0036 & 0.0024 & 0.0001 \\ glass4 & 0.0066 & 0.0008 & 0.0156 & 0.0101 & 0.0006 & 0.0117 & 0.0087 & 0.0065 & 0.0090 & 0.0005 & 0.0003 \\ glass5 & 0.0070 & 0.0006 & 0.0161 & 0.0100 & 0.0006 & 0.0125 & 0.0085 & 0.0067 & 0.0094 & 0.0004 & 0.0003 \\ glass6 & 0.0055 & 0.0009 & 0.0125 & 0.0092 & 0.0005 & 0.0095 & 0.0067 & 0.0054 & 0.0076 & 0.0007 & 0.0002 \\ new-thyroid1 & 0.0051 & 0.0017 & 0.0116 & 0.0091 & 0.0004 & 0.0074 & 0.0063 & 0.0050 & 0.0069 & 0.0008 & 0.0002 \\ newthyroid2 & 0.0051 & 0.0016 & 0.0114 & 0.0091 & 0.0004 & 0.0073 & 0.0066 & 0.0050 & 0.0068 & 0.0008 & 0.0002 \\ ecoli-0\_vs\_1 & 0.0109 & 0.0028 & 0.0069 & 0.0176 & 0.0002 & 0.0058 & 0.0047 & 0.0024 & 0.0049 & 0.0026 & 0.0001 \\ ecoli-0-6\_7\_vs\_5 & 0.0066 & 0.0010 & 0.0149 & 0.0090 & 0.0005 & 0.0098 & 0.0084 & 0.0061 & 0.0087 & 0.0005 & 0.0003 \\ ecoli-0-6\_7\_vs\_3-5 & 0.0066 & 0.0011 & 0.0150 & 0.0098 & 0.0005 & 0.0102 & 0.0085 & 0.0061 & 0.0088 & 0.0005 & 0.0003 \\ ecoli-0-2-6\_7\_vs\_3-5 & 0.0066 & 0.0012 & 0.0153 & 0.0099 & 0.0005 & 0.0105 & 0.0089 & 0.0063 & 0.0093 & 0.0006 & 0.00003 \\ ecoli-01-0\_1\_5 & 0.0077 & 0.0010 & 0.0100 & 0.0006 & 0.0112 & 0.0093 & 0.0096 & 0.0094 & 0.0005 & 0.0003 \\ ecoli-01-0\_1\_vs\_2-3\_5 & 0.0073 & 0.0012 & 0.0172 & 0.0111 & 0.0006 & 0.0113 & 0.0100 & 0.0068 & 0.0096 & 0.0006 & 0.00003 \\ ecoli-0-3-4\_7\_vs\_5-6 & 0.0082 & 0.0014 & 0.0177 & 0.0117 & 0.0006 & 0.0126 & 0.0104 & 0.0071 & 0.0099 & 0.0006 & 0.0003 \\ ecoli-0-1-4\_6\_vs\_5 & 0.0088 & 0.0010 & 0.0209 & 0.0118 & 0.0070 & 0.0137 & 0.0122 & 0.0082 & 0. Based on this experiment, we conclude that the methods that obtain the best oversampling quality in the previous experiment are among the slowest. By contrast, the fastest methods obtain lower oversampling quality, although it is similar to that of the methods that obtain the best oversampling results.