## 5 Results and Analysis
### Ensemble-1 Results  
Feature weightage obtained from individual scoring functions are consolidated through an averaging operation. The intuition behind the consolidation process is that the final scores will reflect both low-cost and high-accuracy characteristics of the individual functions. Figure 10 shows the feature-cost interaction graph for ensemble-1.  
As can be seen in Figure 10 shows that the point of intersection is around feature number 3. Therefore, all the features starting from feature number 6 up to feature number 3, i.e., 6, 15, 20, 19, 18, and 3, would be selected. In this regard, Table 10 shows the results of evaluation metrics for features selected by the ensemble-1 approach.  
A cursory glance at Tables 9 and 10 shows that the proposed ensemble-1 technique successfully reduced the overall cost while improving the key evaluation metrics over the individual scoring  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Measure** & **AUC** & **Cost** \\ \hline NB & 98.25 \(\pm\) 2.92\% & 95.72 \(\pm\) 3.81\% & 98.78 \(\pm\) 1.64\% & 91.94 \(\pm\) 7.55\% & 97.14 \(\pm\) 2.21\% & 0.995 \(\pm\) 0.006 & \\ LG & 99.10 \(\pm\) 1.90\% & 98.80 \(\pm\) 2.80\% & 100.0 \(\pm\) 0.0\% & 97.50 \(\pm\) 5.60\% & 99.40 \(\pm\) 1.40\% & 1.0 \(\pm\) 0.0 & \\ DL & 97.40 \(\pm\) 2.40\% & 96.10 \(\pm\) 3.60\% & 100.0 \(\pm\) 0.0\% & 93.10 \(\pm\) 6.40\% & 98.0 \(\pm\) 1.90\% & 1.0 \(\pm\) 0.0 & \\ DT & 89.40 \(\pm\) 4.0\% & 87.07 \(\pm\) 5.20\% & 97.10 \(\pm\) 3.90\% & 76.40 \(\pm\) 12.3\% & 92.10 \(\pm\) 2.80\% & 0.975 \(\pm\) 0.03 & 95.05 \\ RF & 98.30 \(\pm\) 2.4\% & 97.50 \(\pm\) 3.4\% & 100.0 \(\pm\) 0.0\% & 95.0 \(\pm\) 6.80\% & 98.70 \(\pm\) 1.0\% & 1.0 \(\pm\) 0.0 & \\ GBT & 95.70 \(\pm\) 4.3\% & 93.80 \(\pm\) 6.1\% & 100 \(\pm\) 0.0\% & 88.30 \(\pm\) 11.9\% & 96.70 \(\pm\) 3.20\% & 1.0 \(\pm\) 0.0 & \\ SVM & 95.60 \(\pm\) 3.1\% & 96.20 \(\pm\) 5.6 \% & 97.20 \(\pm\) 3.8\% & 93.30 \(\pm\) 9.90\% & 96.50 \(\pm\) 2.40\% & 0.993 \(\pm\) 0.009 & \\ Average & 96.25 \(\pm\) 2.82\% & 95.72 \(\pm\) 3.81\% & 98.78 \(\pm\) 1.64\% & 91.94 \(\pm\) 7.55\% & 97.14 \(\pm\) 2.21\% & 0.995 \(\pm\) 0.006 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ensemble-1 results based on selected features.  
Figure 10: Ensemble-1 based feature scoring.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scoring** & **Average** & **Average** & **Average** & **Average** & **Average** & **Average** & **Average AUC** & **Cost** \\
**Function** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Measure** & **Average AUC** & **Cost** \\ \hline DT Only & 71.95 \(\pm\) 3.44\% & 70.21 \(\pm\) 2.94\% & **99.01 \(\pm\) 1.28\%** & 27.32 \(\pm\) 7.05\% & 81.78 \(\pm\) 2.27\% & 0.937 \(\pm\)0.056 & **123.29** \\ RF Only & **96.01 \(\pm\) 2.64\%** & **95.30 \(\pm\) 2.71\%** & 98.78 \(\pm\) 1.91\% & **90.42 \(\pm\) 5.92\%** & **96.98 \(\pm\) 2.25\%** & **0.995 \(\pm\) 0.006** & 197.05 \\ GBT Only & 85.24 \(\pm\) 3.88\% & 84.71 \(\pm\) 4.58\% & 98.34 \(\pm\) 2.14\% & 63.22 \(\pm\) 13.18\% & 90.25 \(\pm\) 3.01\% & 0.941 \(\pm\) 0.05 & 150.25 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Averaged evaluation results for individual scoring functions on seven classification models.  
functions. Moreover, multiple combinations of feature scoring functions and their respective averaged results over seven classification models are shown in Table 11.