## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Logistic Regression** & \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**Performance Metrics (Accuracy)/Dataset**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Variation**} \\ \cline{3-8}  & & **CM1** & **KC1** & **KC3** & **MW1** & **PC2** & **(\%)** & **(\%)** \\ \hline
**NO Feature Selection** & LR & 85.32 & 76.76 & 82.47 & 88.00 & 97.09 & 85.93 & 0 \\ \hline
**LR +** CFS + Heuristic Search** & LR+GA & 85.93 & 75.82 & 80.93 & 90 & 97.51 & 86.04 & 0.12 \\
**LR +** ETS + Heuristic Search** & LR+PSO & 85.32 & 75.56 & 81.44 & **91.2** & 97.51 & 86.21 & 0.32 \\
**Heuristic Search** & LR+PSO & **87.16** & 76.16 & 82.47 & 89.6 & 97.65 & 86.61 & 0.79 \\
**Heuristic Search** & LR+FS & 86.24 & 75.47 & **82.99** & 90 & 97.51 & 86.44 & 0.60 \\
**LR +**AS & 85.93 & 75.47 & 82.47 & 88.8 & **97.78** & 86.09 & 0.19 \\ \hline
**LR +** CFS + Exhaustive Search** & LR+GSS & 86.24 & 76.76 & 82.47 & 90 & 97.51 & 86.60 & 0.78 \\
**Exhaustive Search** & LR+BF & 86.85 & 75.90 & 82.47 & 89.2 & 97.65 & 86.41 & 0.56 \\ \hline
**LR +**GA & **87.16** & 76.59 & **82.99** & 90.8 & 97.37 & **86.98** & **1.22** \\
**LR +**BAT & 85.02 & 76.08 & 81.44 & 89.6 & 97.51 & 85.93 & 0.00 \\
**Heuristic Search** & LR+PSO & 85.02 & 76.51 & 81.44 & 90.4 & 97.65 & 86.20 & 0.32 \\
**Heuristic Search** & LR+FS & 85.32 & 76.51 & 82.47 & 90 & 97.23 & 86.31 & 0.44 \\
**LR +**AS & 84.71 & **77.11** & 81.96 & 90.4 & 97.23 & 86.28 & 0.41 \\ \hline
**LR +** CNS + Exhaustive Search** & LR+GSS & 86.54 & 76.33 & 81.96 & 89.6 & 97.65 & 86.42 & 0.57 \\
**Exhaustive Search** & LR+BF & **87.16** & 76.51 & 81.96 & 90.4 & **97.78** & 86.76 & 0.97 \\ \hline
**LR +**IG & 86.54 & 75.47 & 81.96 & 90.4 & 97.51 & 86.38 & 0.52 \\
**LR +**Filter Method** & LR+RF & **87.16** & 76.42 & 80.93 & 89.2 & 97.65 & 86.27 & 0.40 \\
**LR +**GR & **87.16** & 75.82 & 81.96 & 88.8 & 97.09 & 86.16 & 0.27 \\
**LR +**CV & 86.85 & 75.65 & 80.41 & 90.4 & 97.51 & 86.16 & 0.27 \\ \hline \hline \end{tabular} Boldface typeface indicates the highest value for each dataset.  
\end{table} 
Table 8: Accuracy Values of Logistic Regression Classifier on Full and Reduced Datasets. Specifically, considering the average accuracy performance value of prediction models based on NB classifier as shown in Table 6, NB with CFS using BAT heuristic search method had the highest average accuracy value, i.e., 84.65%. This accuracy value is better than when no FS methods are used with the NB model (81.14%) by 4.33%. The same goes to prediction models based on DT classifier, as shown in Table 7. DT with CFS using BAT heuristic search method had the best average accuracy value of 86.66%. Compared with when no FS methods on DT (84.50%), a variation of 2.33% increment was observed. In the case of LR classifier, as presented in Table 8, LR with CNS based on GS had the highest average accuracy value of 86.98% with a positive variation of 1.22% when compared with when no FS methods are used on LR (85.93%). From Table 9, KNN with CNS based on BFS had the highest average accuracy value of 83.70% with a positive variation of 3.92% when compared with no FS methods. It was also observed that LR with CNS based on GS had the highest average accuracy value (85.93%) across all prediction models and NB with CFS based on BAT heuristic search had the highest positive variation (4.33%). This clearly shows that FS methods have a positive effect on the prediction models as the average accuracy values based on each classifier without FS methods are less than when FS methods are applied. Our findings on the positive effect of FS methods on prediction models are in accordance with research outcomes from existing empirical studies. [PERSON] et al. [28], [PERSON] and [PERSON] [26], and [PERSON] et al. [18] in their respective studies also reported that FS methods had a positive effect on prediction models in SDP. However, our study explored the effect of FS methods on prediction models based on the search method which is different from existing empirical studies.