## 2 Multiple-Criteria Decision-Making
### The TOPSIS Method
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Acc** & **RMSE** & **TP-Rate** & **TN-Rate** & **AUC** & **G-Mean** & **F-Measure** \\ \hline Bnet & 0.89 & 0.33 & 0.80 & 0.98 & 0.93 & 0.89 & 0.89 \\ NBC & 0.87 & 0.36 & 0.77 & 0.98 & 0.90 & 0.87 & 0.87 \\ logR & 0.89 & 0.30 & 0.85 & 0.94 & 0.93 & 0.89 & 0.89 \\ MLP & 0.85 & 0.34 & 0.78 & 0.92 & 0.90 & 0.85 & 0.85 \\ SVM & 0.78 & 0.47 & 0.75 & 0.81 & 0.78 & 0.78 & 0.78 \\ RBF & 0.78 & 0.39 & 0.58 & **0.99** & 0.83 & 0.76 & 0.78 \\ INN & 0.77 & 0.48 & 0.75 & 0.79 & 0.77 & 0.77 & 0.77 \\ RIPPER & 0.89 & 0.30 & 0.81 & 0.97 & 0.92 & 0.89 & 0.89 \\ C4.5 & 0.87 & 0.33 & 0.84 & 0.91 & 0.90 & 0.87 & 0.87 \\ randF & **0.90** & **0.27** & **0.85** & 0.95 & **0.95** & **0.90** & **0.90** \\ \hline \end{tabular}
\end{table} 
Table 6: Performance results for the sabiSPQ database. \begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Acc** & **RMSE** & **TP-rate** & **TN-rate** & **AUC** & **G-mean** & **F-measure** \\ \hline Bnet & 0.73 & 0.49 & 0.71 & **0.76** & 0.82 & 0.73 & 0.73 \\ NBC & 0.69 & 0.53 & **0.89** & 0.46 & 0.75 & 0.64 & 0.67 \\ logR & 0.74 & 0.44 & 0.76 & 0.71 & 0.81 & 0.73 & 0.74 \\ MLP & 0.74 & 0.45 & 0.81 & 0.65 & 0.81 & 0.73 & 0.74 \\ SVM & 0.71 & 0.54 & 0.67 & 0.75 & 0.71 & 0.71 & 0.71 \\ RBF & 0.71 & 0.43 & 0.80 & 0.62 & 0.80 & 0.70 & 0.71 \\ INN & 0.75 & 0.50 & 0.77 & 0.73 & 0.75 & 0.75 & 0.75 \\ RIPPER & 0.74 & 0.44 & 0.76 & 0.71 & 0.77 & 0.73 & 0.74 \\ C4.5 & 0.69 & 0.49 & 0.67 & 0.71 & 0.71 & 0.69 & 0.69 \\ randF & **0.79** & **0.39** & 0.83 & 0.74 & **0.86** & **0.78** & **0.79** \\ \hline \end{tabular}
\end{table} 
Table 7: Performance results for the Polish database. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Acc** & **RMSE** & **TP-Rate** & **TN-Rate** & **AUC** & **TN-Rate** & **F-Measure** \\ \hline Bnet & 0.85 & 0.34 & 0.81 & 0.89 & 0.91 & 0.85 & 0.85 \\ NBC & 0.77 & 0.44 & 0.59 & **0.92** & 0.89 & 0.74 & 0.76 \\ logR & **0.87** & 0.32 & **0.88** & 0.86 & **0.93** & **0.87** & **0.87** \\ MLP & 0.83 & 0.38 & 0.82 & 0.84 & 0.90 & 0.83 & 0.83 \\ SVM & 0.86 & 0.38 & 0.93 & 0.80 & 0.86 & 0.86 & 0.86 \\ RBF & 0.81 & 0.36 & 0.73 & 0.89 & 0.90 & 0.81 & 0.81 \\ INN & 0.81 & 0.43 & 0.80 & 0.83 & 0.81 & 0.81 & 0.82 \\ RIPPER & **0.87** & 0.34 & 0.84 & 0.89 & 0.88 & 0.86 & **0.87** \\ C4.5 & 0.86 & 0.36 & 0.83 & 0.88 & 0.86 & 0.85 & 0.86 \\ randF & **0.87** & **0.31** & 0.87 & 0.87 & **0.93** & **0.87** & **0.87** \\ \hline \end{tabular}
\end{table} 
Table 5: Performance results for the Australian database. The conflicting points related to the employment of single performance assessment criteria led to carry out some experiments with the MCDM methods included in this study. Taking into account that identifying relative weights of criterion importance is nontrivial, one can use either subjective weighting methods or objective weighting methods [45]. While the subjective methods determine weights solely according to the decision-maker's judgments/preferences, the objective methods define weights by solving mathematical models automatically without any consideration of the decision maker's preferences. In general, objective weighting is applied to situations where reliable subjective weights cannot be obtained [46]. In this work, the weights used by the TOPSIS and PROMETHE methods were set in line with the relative relevance of the performance evaluation measures for credit granting decision problems. For instance, AUC, G-mean, and F-measure have traditionally been deemed as significant performance