## 7 Results
### Results of Deep Reinforcement for Imbalanced CCF Dataset  
In this section, the performances of DRL for the imbalanced CCF dataset are discussed. The confusion matrix, which is shown in Table 15, illustrates that the TP samples are significantly less than the TN samples. The TP samples are just 37, while the TN samples are approximately 29,701.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ML Algorithms** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**F1 Score**} & \multirow{2}{*}{**G-Mean**} & \multicolumn{2}{c}{**Balanced Accuracy**} & \multirow{2}{*}{**MCC**} & \multirow{2}{*}{**AUC**} \\
**Based on SMOTE** & & & & & & & & Accuracy & MCC \\ \hline RF approach 1 & 99.99\% & 99.98\% & 100.00\% & 99.98\% & 99.99\% & 99.99\% & 99.98\% & 100.00\% \\ RF approach 2 & 99.95\% & 89.55\% & 81.63\% & 99.98\% & 85.41\% & 90.34\% & 90.81\% & 85.48\% & 90.81\% \\ KNN approach 1 & 99.88\% & 99.76\% & 100.00\% & 99.75\% & 99.88\% & 99.88\% & 99.88\% & 99.76\% & 100.00\% \\ KNN approach 2 & 99.83\% & 50.00\% & 86.39\% & 99.85\% & 63.34\% & 92.88\% & 93.12\% & 65.65\% & 93.12\% \\ DT approach 1 & 99.78\% & 99.68\% & 99.87\% & 99.68\% & 99.78\% & 99.78\% & 99.78\% & 99.55\% & 99.80\% \\ DT approach 2 & 99.76\% & 40.21\% & 76.87\% & 99.80\% & 52.80\% & 87.59\% & 88.34\% & 55.50\% & 88.34\% \\ LR approach 1 & 94.51\% & 97.36\% & 91.51\% & 97.51\% & 94.34\% & 94.46\% & 94.51\% & 89.17\% & 98.90\% \\ LR approach 2 & 97.53\% & 6.04\% & 91.84\% & 97.54\% & 11.34\% & 94.64\% & 94.69\% & 23.21\% & 94.68\% \\ AdaBoost approach 1 & 97.24\% & 98.25\% & 96.20\% & 98.28\% & 97.21\% & 97.24\% & 97.24\% & 94.50\% & 97.24\% \\ AdaBoost approach 2 & 98.71\% & 10.54\% & 87.07\% & 98.73\% & 18.81\% & 92.72\% & 92.90\% & 30.05\% & 92.90\% \\ XGBoost approach 1 & 99.97\% & 99.94\% & 99.94\% & 100\% & 99.97\% & 99.97\% & 99.97\% & 99.94\% & 99.67\% \\ XGBoost approach 2 & 99.93\% & 76.88\% & 83.67\% & 100\% & 80.13\% & 91.45\% & 91.82\% & 80.17\% & 91.82\% \\ DNN approach 1 & 99.81\% & 99.69\% & 99.92\% & 99.69\% & 99.81\% & 99.81\% & 99.81\% & 99.61\% & 99.97\% \\ DNN approach 2 & 99.76\% & 41.18\% & 85.71\% & 99.79\% & 55.63\% & 92.48\% & 92.75\% & 59.32\% & 95.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Classification measurements indexes of ML algorithms based on SMOTE for two resampling approaches.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ML Algorithms** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**F1 Score**} & \multirow{2}{*}{**G-Mean**} & \multicolumn{2}{c}{**Balanced Accuracy**} & \multirow{2}{*}{**MCC**} & \multirow{2}{*}{**AUC**} \\
**Based on ADASYN** & & & & & & & & Accuracy & MCC \\ \hline RF approach 1 & 99.98\% & 99.96\% & 100.00\% & 99.96\% & 99.98\% & 99.98\% & 99.98\% & 99.98\% & 99.96\% & 100.00\% \\ RF approach 2 & 99.95\% & 90.00\% & 79.59\% & 99.98\% & 84.48\% & 89.21\% & 89.79\% & 84.61\% & 89.79\% \\ KNNapproach 1 & 99.90\% & 99.79\% & 100.00\% & 99.7  
Moreover, the classification metrics, such as accuracy, precision, sensitivity, F1 score, G-mean, and balance accuracy, for the DRL with the imbalanced CCF dataset are indicated in Table 16. It is obvious that the DRL does not have good performance in CCF detection. The accuracy index is only about 34.8%, and the precision index is very low, at just 0.067%. The value of the F1 score is also low, at about 13.27%. Generally, the results of the DRL for the imbalanced CCF classification do not indicate good performance compared with the performance of the ML classifiers based on the balanced CCF dataset.