## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**FILTER-BASED SUBSET SELECTION METHODS (FSS)**} \\ \hline \hline \multirow{3}{*}{**Attribute Evaluator**} & \multicolumn{5}{c}{**CfsSubsetEval (CFS) Method**} \\ \cline{3-7}  & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{5}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**Heuristics Method**} & GA & 2 & 80.41 & **82.99** & 80.93 & 78.87 \\  & BAT & 2 & 81.96 & **82.99** & 81.44 & 78.87 \\  & PSO & 3 & 79.90 & 81.96 & 82.47 & 75.77 \\  & FS & 3 & 80.41 & 80.41 & **82.99** & 74.23 \\  & AS & 2 & 80.41 & 82.47 & 82.47 & 75.26 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & GSS & 6 & 80.93 & 80.41 & 82.47 & 77.32 \\  & BF & 3 & 79.90 & 81.44 & 82.47 & 75.77 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & Average & 80.56 & 81.81 & **82.18** & 76.58 \\ \cline{3-7}  & \multirow{2}{*}{**Search Methods**} & & **No. of Features** & \multicolumn{5}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**Heuristics Method**} & GA & 2 & 80.41 & **82.99** & 80.93 & 78.87 \\  & BAT & 2 & 81.96 & **82.99** & 81.44 & 78.87 \\ \cline{1-1}  & PSO & 3 & 79.90 & 81.96 & 82.47 & 75.77 \\ \cline{1-1}  & FS & 3 & 80.41 & 80.41 & **82.99** & 74.23 \\ \cline{1-1}  & AS & 2 & 80.41 & 82.47 & 82.47 & 75.26 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & GSS & 6 & 80.93 & 80.41 & 82.47 & 77.32 \\ \cline{1-1}  & BF & 3 & 79.90 & 81.44 & 82.47 & 75.77 \\ \hline \hline \multirow{3}{*}{**Exhaustive Method**} & Average & 80.56 & 81.81 & **82.18** & 76.58 \\ \cline{1-1} \cline{2-1}  & \multirow{2}{*}{**Average**} & & **80.56** & **81.81** & **82.18** & 76.58 \\ \cline{1-1} \cline{2-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance Accuracy Values of FS-based Prediction Models on KC1 dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**FITTER-BASED SUBSET SELECTION METHODS (FSS)**} \\ \hline \hline \multirow{3}{*}{**Attribute Evaluator**} & \multicolumn{6}{c}{**CfsSubsetEval (CFS) Method**} \\ \cline{2-7}  & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{3}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**Heuristics Method**} & GA & 8 & 84.4 & 89.6 & 90 & 84 \\  & BAT & 9 & 86 & 90.4 & **91.2** & 84.4 \\  & PSO & 7 & 84.8 & 89.2 & 89.6 & 84 \\  & FS & 9 & 84.8 & 88.4 & 90 & 84 \\  & AS & 7 & 85.2 & 89.6 & 88.8 & 82 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & GSS & 6 & 84.4 & 89.2 & 90 & 84 \\  & BF & 7 & 84.8 & 89.6 & 89.2 & 82.8 \\ \hline \multirow{3}{*}{**Attribute Evaluator**} & \multicolumn{6}{c}{**CnsistencySubsetEval (CNS)Method**} \\ \cline{2-7}  & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{3}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**Heuristics Method**} & GA & 11 & 85.2 & 89.2 & 90.8 & 83.6 \\  & BAT & 17 & 84 & 89.2 & 89.6 & 84.4 \\  & PSO & 8 & 85.6 & 88.4 & 90.4 & 82.8 \\  & FS & 17 & 83.6 & 89.6 & 90 & 82.8 \\  & AS & 13 & 83.6 & 89.2 & 90.4 & 83.6 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & GSS & 6 & 86 & **91.6** & 89.6 & 85.6 \\  & BF & 9 & 84.4 & 89.6 & 90.4 & 84.4 \\ \hline \multicolumn{7}{c}{Average} & \multicolumn{3}{c}{84.63} & 89.54 & **90.17** & 83.89 \\ \hline \multicolumn{7}{c}{**FITTER-BASED FEATURE RANKING METHODS (FFR)**} \\ \hline \hline \multirow{3}{*}{**Attribute Evaluator**} & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No.