# Code Smell Detection Using Ensemble Machine Learning Algorithms
## 4 The Outcome of Proposed Algorithms
### Performance Comparisonbetween Five Ensemble and Two Deep Learning Methods  
This section compares the outcomes of all five used ensemble and two deep learning techniques applied on ten features selected by the Chi-square FSAs. Table 12 shows \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **PPV** & **Sensitivity** & **F-Measure** & **AUC\_ROC\_Score** & **Accuracy (\%)** & **MCC** & **Cohen\_Kappa** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Accuracy (\%)** & **(\%)** & **(\%)** \\ \hline DC & 96 & 96 & 96 & 93.64 & 97.82 & 95.52 & 95.12 \\ \hline FC & 98 & 98 & 98 & 97.28 & 97.98 & 99.12 & 99.08 \\ \hline FL & 100 & 99 & 100 & 99.12 & 99.08 & 99.12 & 99.08 \\ \hline LM & 100 & 100 & 99 & 99.29 & 99.26 & 98.78 & 98.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of ANN algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Datasets** & **PPV** & **Sensitivity** & **F-Measure** & **AUC\_ROC\_Score** & **Accuracy (\%)** & **MCC** & **Cohen\_Kappa** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Accuracy (\%)** & **(\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline DC & 98 & 99 & 98 & 93.64 & 97.82 & 95.52 & 95.12 \\ \hline FC & 99 & 99 & 98 & 92.89 & 97.23 & 96.26 & 96.12 \\ \hline FE & 100 & 99 & 100 & 99.12 & 99.08 & 99.12 & 99.08 \\ \hline LM & 100 & 100 & 99 & 99.29 & 99.26 & 98.78 & 98.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of CNN algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Datasets** & **PPV** & **Sensitivity** & **F-Measure** & **AUC\_ROC\_Score** & **Accuracy (\%)** & **MCC** & **Cohen\_Kappa** (\%)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline DC & 100 & 100 & 100 & 92.40 & 98.80 & 94.89 & 94.89 \\ \hline GC & 99 & 99 & 99 & 91.84 & 97.62 & 92.66 & 92.62 \\ \hline FE & 100 & 100 & 100 & 97.25 & 95.74 & 90.20 & 89.72 \\ \hline LM & 100 & 100 & 100 & 95.6 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of Gradient boosting algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Algorithms** & \multicolumn{3}{c}{**DC**} & \multicolumn{3}{c}{**GC**} & \multicolumn{3}{c}{**FE**} & \multicolumn{3}{c}{**LM**} \\ \cline{2-11}
**F (\%)** & **AUC (\%)** & **A (\%)** & **F (\%)** & **AUC (\%)** & **A (\%)** & **F (\%)** & **AUC (\%)** & **A (\%)** & **F (\%)** & **AUC (\%)** & **AUC (\%)** \\ \hline AdaBoost & 99.00 & 84.26 & 98.80 & 97.00 & 87.97 & 97.62 & 100 & 98.72 & 100 & 100 & 98.98 & 100 \\ \hline Bagging & 100 & 97.92 & 98.80 & 100 & 98.92 & 97.62 & 100 & 94.20 & 100 & 100 & 88.80 & 99.94 \\ \hline Max voting & 100 & 94.62 & 98.81 & 98.00 & 85.24 & 97.62 & 100 & 97.67 & _97.87_ & 100 & 97.62 & 97.92 \\ \hline Gradient Boosting & 100 & 92.40 & 98.80 & 99.00 & 91.84 & 97.62 & 100 & 97.25 & 95.74 & 100 & 95.66 & 100 \\ \hline XGBost & 100 & 92.54 & 99.80 & 99.00 & 87.26 & 97.62 & 100 & 93.40 & 100 & 100 & 84.33 & 100 \\ \hline ANN & 96.00 & 93.64 & 97.82 & 96.00 & 92.89 & 97.23 & 98.00 & 97.28 & 97.98 & 98.00 & 96.29 & 98.25 \\ \hline CNN & 98.00 & 93.64 & 97.82 & 98.00 & 92.89 & 97.23 & 100 & 99.12 & 99.08 & 99.00 & 99.29 & 99.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison Performance between Five Ensemble and Two Deep Learning Methods scale smells Datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **PPV** & **Sensitivity** & **F-Measure** & **AUC\_ROC\_Score** & **Accuracy (\%)** & **MCC** & **Cohen\_Kappa** (\%)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline DC & 100 & 100 & 100 & 92.54 & 99.80 & 100 & 100 \\ \hline GC & 98 & 99 & 99 & 87.26 & 97.62 & 94.69 & 94.54 \\ \hline FE & 100 & 100 & 100 & 93.40 & 100 & 92.36 & 92.07 \\ \hline LM & 100 & 100 & 100 & 84.33 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of XGboost algorithm.