## 4 Experimental Results
That is, the performance of FS methods depends largely on the dataset as the best subset of features that varied from one dataset to another. In addition, it was also observed that based on the FSS (CFS and CNS) methods, a varying number of features were selected. This presents a very interesting case on how the small number of features affects the performance of prediction models. Some studies argued that the lesser the features, the better the performance [26; 37; 43]. However, in this study, it was observed that the number of features selected depends largely on the FS method used. CNS often selects more features and the prediction models based on CNS outperforms other FS methods. In addition, as presented in Table 15, considering the FFR methods, IG had the best influence on the prediction models over other FFR methods. While considering the FSS, CNS based on BFS had the best influence on the prediction models. However, CFS based on BS had the best improvement on the performance of NB and DT and CNS based on GS and BF improved the performance of LR and KNN best, respectively. \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 & 5 & 94.18 & 97.51 & 97.51 & 96.54 \\  & BAT & 5 & 95.01 & **97.78** & 97.51 & 96.12 \\  & PSO & 5 & 94.46 & 97.51 & 97.65 & 96.81 \\  & FS & 5 & 93.91 & 97.65 & 97.51 & 95.84 \\  & AS & 6 & 95.57 & 97.51 & **97.78** & 95.29 \\ \hline \multirow{2}{*}{**Exhaustive Method**} & GSS & 6 & 93.91 & 97.51 & 97.51 & 96.40 \\  & BF & 5 & 94.32 & 97.51 & 97.65 & 96.81 \\ \hline \multicolumn{6}{c}{Average} & \multicolumn{4}{c}{94.48} & 97.57 & **97.59** & 96.26 \\ \hline \hline \multicolumn{6}{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 & 15 & 94.18 & 97.65 & 97.37 & 96.26 \\  & BAT & 17 & 94.46 & 97.51 & 97.51 & 96.12 \\  & PSO & 9 & 94.87 & 97.65 & 97.65 & 97.09 \\  & FS & 17 & 93.63 & 97.65 & 97.23 & 96.12 \\  & AS & 16 & 94.46 & 97.37 & 97.23 & 96.40 \\ \hline \multirow{2}{*}{**Exhaustive Method**} & GSS & 6 & 93.91 & 97.65 & 97.65 & 95.84 \\  & BF & 1 & **97.78** & **97.78** & **97.78** & **97.78** \\ \hline \multicolumn{6}{c}{Average} & \multicolumn{4}{c}{94.76} & **97.61** & 97.49 & 96.52 \\ \hline \hline \multicolumn{6}{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}{*}{**IG**} & Ranker & 6 & 93.91 & 97.65 & 97.51 & 96.26 \\  & Ranker & 6 & 96.68 & **97.78** & 97.65 & 96.12 \\  & Ranker & 6 & 94.18 & **97.78** & 97.09 & 96.12 \\  & **CV** & Ranker & 6 & 95.84 & **97.78** & 97.51 & 95.98 \\ \hline \multicolumn{6}{c}{Average} & \multicolumn{4}{c}{95.15} & **97.75** & 97.44 & 96.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance Accuracy Values of FS-based Prediction Models on PC2 dataset. Furthermore, we conducted a stability test on the FS methods based on different prediction models using the average accuracy values from the experimental results. We calculated the Standard Deviation (SD) and the Co-efficient of Variation (CV) as presented in Appendix A Table A1. FFR methods produced more stable results in terms of accuracy across the prediction models as compared with the FSS methods.