## 3 Design and Implementation of the Proposed CBC Method
### Results and Analysis of the Proposed CBC Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cluster** & **Model** & **Accuracy** & **Precision** & **Detection Rate** & **F1 Score** \\ \hline \multirow{4}{*}{0} & XGBoost & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\  & AdaBoost & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\  & KNN & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\  & Random Forest & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\ \hline \multirow{4}{*}{1} & XGBoost & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\  & AdaBoost & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\  & KNN & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\  & Random Forest & 1.000000 & 1.000000 & 1.000000 & 1.000000 \\ \hline \multirow{4}{*}{2} & XGBoost & 0.987930 & 0.663649 & 0.948756 & 0.737911 \\  & AdaBoost & 0.972887 & 0.589327 & 0.595256 & 0.645394 \\  & KNN & 0.992309 & 0.713865 & 0.869649 & 0.770486 \\  & Random Forest & 0.989390 & 0.678963 & 0.949490 & 0.754387 \\ \hline \multirow{4}{*}{3} & XGBoost & 0.991504 & 0.628437 & 0.785065 & 0.676433 \\  & AdaBoost & 0.987413 & 0.592226 & 0.802163 & 0.639682 \\  & KNN & 0.991661 & 0.616537 & 0.727689 & 0.653748 \\  & Random Forest & 0.993864 & 0.673548 & 0.786249 & 0.715849 \\ \hline \multirow{4}{*}{4} & XGBoost & 0.988463 & 0.723924 & 0.965289 & 0.800836 \\  & AdaBoost & 0.985856 & 0.699919 & 0.984061 & 0.780742 \\  & KNN & 0.989097 & 0.721412 & 0.808791 & 0.757659 \\  & Random Forest & 0.989728 & 0.738862 & 0.957673 & 0.812737 \\ \hline \multirow{4}{*}{5} & XGBoost & 0.713226 & 0.753350 & 0.673216 & 0.669246 \\  & AdaBoost & 0.690977 & 0.707043 & 0.654948 & 0.651708 \\  & KNN & 0.700453 & 0.727754 & 0.662008 & 0.657897 \\  & Random Forest & 0.714050 & 0.760801 & 0.672641 & 0.667631 \\ \hline \multirow{4}{*}{6} & XGBoost & 0.992569 & 0.557205 & 0.809048 & 0.595220 \\  & AdaBoost & 0.989255 & 0.555924 & 0.963414 & 0.597296 \\ \cline{1-1}  & KNN & 0.994986 & 0.588485 & 0.841464 & 0.639768 \\ \cline{1-1}  & Random Forest & 0.994448 & 0.575487 & 0.80989 & 0.620558 \\ \hline \multirow{4}{*}{7} & XGBoost & 0.927954 & 0.662980 & 0.783800 & 0.702961 \\ \cline{1-1}  & AdaBoost & 0.925072 & 0.685871 & 0.930986 & 0.747481 \\ \cline{1-1}  & KNN & 0.916426 & 0.604472 & 0.658799 & 0.624136 \\ \cline{1-1}  & Random Forest & 0.930837 & 0.681151 & 0.844789 & 0.731424 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of 2-class classification mapped to 8 clusters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Model** & **Accuracy** & **Precision** & **Detection Rate** & **F1 Score** \\ \hline \multirow{4}{*}{**Cluster 0**} & XGBoost & 0.655465011 & 0.427524392 & 0.433726346 & 0.40504772 \\ \cline{2-5}  & AdaBoost & 0.357545828 & 0.37869124 & 0.399037193 & 0.323317814 \\ \cline{2-5}  & KNN & 0.765407372 & 0.459344396 & 0.380815535 & 0.385191394 \\ \cline{2-5}  & Random Forest & 0.657743055 & 0.427780721 & 0.429906529 & 0.405431469 \\ \hline \multirow{4}{*}{**Cluster 1**} & XGBoost & 0.99983871 & 0.999919329 & 0.833333333 & 0.899959661 \\ \cline{1-1}  & AdaBoost & 0.999838781 & 0.66666667 & 0.555555556 & 0.6 \\ \cline{1-1} \cline{2-5}  & KNN & 0.999516129 & 0.666666667 & 0.444390655 & 0.499973103 \\ \cline{1-1} \cline{2-5}  & Random Forest & 0.999677419 & 0.666666667 & 0.44444444 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of 8-class classification mapped to 2 clusters. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Detection Rate** & **F1 Score** \\ \hline XGBoost & 0.950205881 & 0.748692557 & 0.870646623 & 0.772826244 \\ \hline AdaBoost & 0.943932506 & 0.728788814 & 0.911921817 & 0.757562012 \\ \hline KNN & 0.948116543 & 0.746565651 & 0.821049809 & 0.762961635 \\ \hline Random Forest & 0.951539631 & 0.763596814 & 0.87760408 & 0.787823389 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Aggregated results of 2-class classification mapped to 8 clusters. Figure 8: Accuracy comparison of 8-class classification with 2 clusters.