# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 5 Experimental Results
### Results of the Sampling Algorithms
On the other hand, the NCL algorithm showed good results for datasets having IR\(<\)4, and bad results for the remaining datasets. In addition, all the compared algorithms but RUS inverted the classes in several datasets (converting the majority class into the minority one). In the following, the results of the hybrid algorithms are offered in Table 8, for each of the datasets analyzed in the present investigation. The cases in which the classes were invested are shown in italics (the minority class became the majority), and in bold the good results (those with an imbalance closer to 1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Original** & **CNN** & **CNNTL** & **NCL** & **OSS** & **RUS** & **SBC** & **TL** \\ \hline Australian & 1.25 & _3.37_ & _8.59_ & _1.35_ & _5.50_ & **1.00** & 2.22 & 1.04 \\ Default credit & 3.52 & **1.13** & _2.24_ & **1.78** & _1.21_ & **1.00** & - & 2.69 \\ German & 2.33 & _1.12_ & _2.96_ & **1.08** & _1.75_ & **1.00** & 1.96 & **1.68** \\ Give me credit & 13.96 & _1.59_ & _1.36_ & 10.84 & 1.39 & **1.00** & _2.00_ & 12.95 \\ Iranian & 19.00 & 2.37 & **1.47** & 14.53 & 2.18 & **1.00** & - & 17.91 \\ Japanese & 1.26 & 3.45 & _9.07_ & _1.33_ & _5.30_ & **1.00** & 1.96 & _1.01_ \\ Polish\_1 & 24.93 & 2.53 & **1.40** & 21.14 & 2.50 & **1.00** & - & 23.70 \\ Polish\_2 & 24.43 & 2.62 & **1.41** & 20.31 & 2.56 & **1.00** & - & 22.97 \\ Polish\_3 & 20.22 & 2.58 & **1.33** & 16.33 & 2.45 & **1.00** & - & 18.86 \\ Polish\_4 & 18.01 & 2.38 & **1.25** & 14.42 & 2.22 & **1.00** & - & 16.72 \\ Polish\_5 & 13.41 & **1.84** & **1.15** & 10.23 & **1.62** & **1.00** & - & 12.28 \\ Qualitative & 1.34 & _6.36_ & _5.85_ & _1.28_ & _6.27_ & **1.00** & - & **1.32** \\ The PAKDD & 4.12 & **1.51** & **1.15** & 2.41 & **1.41** & **1.00** & _2.00_ & 3.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Imbalance ratio for undersampling algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **SMOTE-ENN** & **SMOTE-TL** & **SPIDER** & **SPIDER2** & **Original** \\ \hline Australian & _1.03_ & _1.31_ & _1.17_ & _1.49_ & 1.25 \\ Default credit & _1.39_ & _1.50_ & **1.45** & **1.02** & 3.52 \\ German & _1.19_ & _1.66_ & **1.06** & 1.30 & 2.33 \\ Give me credit & _1.13_ & _1.17_ & 4.22 & 2.62 & 13.96 \\ Iranian & _1.18_ & _1.14_ & 6.18 & 3.89 & 19.00 \\ Japanese & _1.02_ & _1.28_ & _1.15_ & _1.46_ & 1.26 \\ Polish\_1 & _1.24_ & _1.19_ & 6.87 & 4.14 & 24.93 \\ Polish\_2 & _1.28_ & _1.21_ & 6.49 & 3.84 & 24.43 \\ Polish\_3 & _1.32_ & _1.26_ & 5.50 & 3.31 & 20.22 \\ Polish\_4 & _1.30_ & _1.24_ & 5.08 & 3.13 & 18.01 \\ Polish\_5 & 1.29 & _1.25_ & 3.98 & 2.51 & 13.41 \\ Qualitative & **1.01** & _1.01_ & **1.29** & **1.31** & 1.34 \\ The PAKDD & 1.33 & 1.16 & 1.64 & **1.05** & 4.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Imbalance ratio for hybrid algorithms. SMOTE-ENN and SMOTE-TL algorithms obtained very good balances in all cases (IRs from 1.01 to 1.66). SPIDER and SPIDER2 algorithms obtained good results for the datasets having an imbalance ratio lower than four, and bad results in the remaining datasets. However, SPIDER2 algorithm obtained better results than SPIDER for the datasets having high imbalance (IR > 4). The IRs for SPIDER2 range from 2.51 to 4.14, while the ones of SPIDER range from 3.98 to 6.87. In addition, we made a diagram summarizing some of the main characteristics of the compared methods (Figure 4). We include some of the positive and negative characteristics, according to the results obtained for instance sampling. Considering the experiments performed, we can conclude that some hybrid methods tend to obtain good balances; however, these results are at the expense in many cases, of inverting the majority class making it a minority. The next section will analyze how these results impact the performance of classifiers of the associative approach, and if this computational cost is justified in results of better performance.