## 4 Experimental Results
In addition, on dataset KC3 as presented in Table 12, DT with CFS based on BAT and GA search had the highest accuracy value of 82.99% with two features selected while LR with CFS based on BAT search in Table 13 had the best accuracy value on MW1 dataset. In Table 14, DT and LR with CFS based on BAT and AS respectively had an accuracy value of 97.78% on PC2 dataset. The FFR methods also had similar accuracy value on PC2 based on DT with RFA, GR, and CV. Clearly, there was no significant difference in the performance of the FS methods, as their respective performance and effect varies from dataset to dataset and the choice of classification algorithm. This research outcome is related to the findings from [PERSON] et al. [29], [PERSON] et al. [31] and [PERSON] et al. [30]. Although on average, FSS methods proved to be better than FFR methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**FILTER-BASED SUBSET SELECTION METHODS (FSS)**} \\ \hline \multirow{2}{*}{**Attribute Evaluator**} & \multicolumn{4}{c}{**CfsSubsetEval (CFS)Method**} \\ \cline{2-7}  & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{4}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{4}{*}{**Heuristics Method**} & GA & 7 & 84.10 & 85.32 & 85.93 & 77.68 \\  & BAT & 5 & 85.32 & **87.16** & 85.32 & 77.37 \\  & PSO & 8 & 83.18 & 86.54 & **87.16** & 80.43 \\  & FS & 7 & 82.87 & 85.93 & 86.24 & 81.04 \\  & AS & 5 & 86.49 & 85.93 & 85.93 & 78.59 \\ \hline \multirow{2}{*}{**Exhaustive Method**} & GSS & 5 & 83.79 & 86.54 & 86.24 & 78.90 \\  & BF & 5 & 83.18 & 86.54 & 86.85 & 80.43 \\ \hline \multirow{4}{*}{**Attribute Evaluator**} & \multicolumn{4}{c}{**ConsistencySubsetEval (CNS)Method**} \\ \cline{2-7}  & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{4}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{4}{*}{**Heuristics Method**} & GA & 12 & 81.96 & 86.24 & **87.16** & 81.35 \\  & BAT & 12 & 81.65 & 85.63 & 85.02 & 79.20 \\  & PSO & 6 & 82.87 & 86.24 & 85.02 & 79.51 \\  & FS & 15 & 81.35 & 83.49 & 85.32 & 80.73 \\  & AS & 8 & 82.87 & 85.93 & 84.71 & 76.15 \\ \hline \multirow{2}{*}{**Exhaustive Method**} & GSS & 6 & 81.65 & **87.16** & 86.54 & 77.68 \\  & BF & 1 & 85.32 & 86.85 & **87.16** & 85.32 \\ \hline \multicolumn{7}{c}{Average} & 82.53 & **85.93** & 85.85 & 79.99 \\ \hline \multicolumn{7}{c}{**FILTER-BASED FEATURE RANKING METHODS (FFR)**} \\ \hline \multirow{2}{*}{**Attribute Evaluator**} & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{4}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{2}{*}{**Attribute Evaluator**} & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{4}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{2}{*}{**IK**} & [PERSON] & 6 & 84.10 & 86.54 & 86.54 & 77.37 \\  & [PERSON] & 6 & 80.12 & 86.24 & 87.16 & 81.64 \\ \cline{1-1} \cline{2-7}  & [PERSON] & 6 & 84.10 & 86.24 & 87.16 & 76.76 \\ \cline{1-1} \cline{2-7}  & [PERSON] & 6 & 81.96 & **87.46** & 86.85 & 77.98 \\ \hline \multicolumn{7}{c}{Average} & 82.57 & 86.62 & **86.93** & 78.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance Accuracy Values of FS-based Prediction Models on CM1 dataset.