## 5 Empirical Evaluation: Setup and Findings
### Evaluation and Analysis of the Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Methodology**} & \multirow{2}{*}{\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Recall Value** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **F1 Score** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} } \\ \cline{3-8} \multirow{2}{*}{[PERSON] and [PERSON] [25]} & NB & 75.90 & 76.00 & 76.00 & 76.30 \\ \cline{2-8}  & SMO & 76.90 & 77.30 & 76.30 & 77.34 \\ \hline \multirow{8}{*}{[PERSON] et al. [26]} & DT & 70.10 & 70.82 & 70.30 & 70.80 \\ \cline{2-8}  & SVM & 67.10 & 66.50 & 67.10 & 66.50 \\ \cline{2-8}  & KNN & 70.30 & 71.10 & 70.60 & 71.11 \\ \cline{2-8}  & NB & 74.50 & 73.60 & 73.90 & 73.60 \\ \cline{2-8}  & RF & 75.10 & 75.40 & 75.20 & 75.40 \\ \cline{2-8}  & NN & 75.40 & 75.80 & 75.50 & 75.80 \\ \cline{2-8}  & AB & 71.00 & 71.00 & 71.00 & 71.00 \\ \cline{2-8}  & LR & 73.30 & 76.80 & 76.00 & 76.80 \\ \hline [PERSON] et al. [27] & RF & 74.30 & 77.00 & 76.58 & 77.00 \\ \hline \multirow{8}{*}{[PERSON] et al. [29]} & KNN & - & 87.20 & - & 79.80 \\ \cline{2-8}  & LR & - & 70.20 & - & 73.30 \\ \cline{1-1} \cline{2-8}  & NB & - & 66.60 & - & 73.10 \\ \cline{1-1} \cline{2-8}  & SVM+RBF & - & 87.30 & - & 83.20 \\ \cline{1-1} \cline{2-8}  & LR & - & - & - & 77.06 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **[PERSON] et al.** [31] \\ \end{tabular} } & DT & 84.78 & 89.66 & 87.15 & 81.89 \\ \cline{2-8}  & NB & 85.71 & 88.64 & 87.15 & 81.89 \\ \cline{2-8}  & Fuzzy KNN & 91.30 & 95.45 & 93.32 & 90.55 \\ \cline{2-8}  & TFKNN & 93.18 & 93.18 & 93.18 & 90.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The comparative evaluation results of the proposed model with the recent approaches in terms of precision, recall, F1 score, and accuracy for the applied PIDD dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Base** \\ **Classifier** \\ \end{tabular} } & \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Precision**} & \multirow{2}{*}{**Recall**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Specificity**} \\ \cline{3-8}  & & **80/20** & **10-Folds** & **80/20** & **10-Folds** & **80/20** & **10-Folds** \\ \hline \multirow{8}{*}{Decision Tree} & Fine Tree & 71.00 & 80.20 & 78.89 & 78.32 & 68.63 & 72.66 & 53.99 & 61.33 \\ \cline{2-8}  & Medium Tree & 82.00 & 82.00 & 81.19 & 78.39 & 75.82 & 73.58 & 65.36 & 63.27 \\ \cline{2-8}  & Coarse Tree & 87.00 & 87.60 & 79.09 & 76.98 & 76.47 & 74.87 & 69.77 & 68.84 \\ \cline{2-8}  & DT (Tuned) & 84.78 & 89.66 & & 81.89 & & 65.00 & \\ \hline \multirow{8}{*}{Naive Bayes} & Gaussian NB & 82.00 & 83.80 & 78.85 & 79.81 & 73.86 & 75.65 & 63.27 & 66.67 \\ \cline{2-8}  & Kernel NB & 91.00 & 89.40 & 73.98 & 75.38 & 73.20 & 74.09 & 70.00 & 69.71 \\ \cline{2-8}  & NB (Tuned) & 85.71 & 88.64 & & 81.89 & & 66.67 & \\ \hline \multirow{8}{*}{KNN} & Fine KNN & 72.00 & 79.60 & 75.00 & 74.95 & 66.01 & 69.40 & 50.88 & 56.96 \\ \cline{2-8}  & Medium KNN & 86.00 & 88.20 & 79.63 & 75.00 & 76.47 & 73.18 & 68.89 & 67.22 \\ \cline{2-8}  & Coarse KNN & 96.00 & 95.60 & 71.64 & 72.64 & 72.55 & 73.70 & 78.95 & 80.00 \\ \cline{1-1} \cline{2-8}  & Cosine KNN & 86.00 & 86.60 & 81.90 & 76.91 & 78.43 & 74.35 & 70.83 & 67.32 \\ \cline{1-1} \cline{2-8}  & Cubic KNN & 86.00 & 87.60 & 75.44 & 74.87 & 72.55 & 72.79 & 64.10 & 66.12 \\ \cline{1-1} \cline{2-8}  & Weighted KNN & 78.00 & 83.40 & 75.59 & 76.37 & 72.55 & 72.40 & 60.00 & 62.61 \\ \cline{1-1} \cline{2-8}  & Fuzzy KNN & 91.30 & 95.45 & & 90.55 & & 79.49 & \\ \hline
**Proposed** & TFKNN & 93.18 & 93.18 & & 90.63 & & 85.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Micro- vs macro-average ROC curve. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Methodology**} & \multirow{2}{*}{ \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{