## 4 Experimental Results
Furthermore, assessing the accuracy performance of each prediction model on each of the dataset will showcase how these prediction models perform based on different FS methods. Tables 10-14 present the comparisons of FS (FFR and FSS) methods on each of the five datasets respectively. Considering the number of features generated by FS methods, FFR features are pre-calculated based on \(\log_{2}N\) (where \(N\) is the number of features). The number of features for FSS methods are based on the search methods used (Heuristic or Exhaustive). Across all datasets and FS methods used in this study, the number of features generated by CFS is less than CNS. On the CM1 dataset, with respect to CFS, LR with PSO search method and DT with BAT search method had an accuracy value of 87.16% with (LR+CFS+PSO) selecting eight features and the (DT+CFS+BAT) had five features. Same also was \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **K Nearest** \\ **Neighbor (KNN)** \\ \end{tabular} } & \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**Performance Metrics (Accuracy)/Dataset**} & \multicolumn{2}{c}{**Average**} & \multicolumn{1}{c}{**Variation**} \\ \cline{3-8}  & & **CM1** & **KC1** & **KC3** & **MW1** & **PC2** & **(\%)** & **(\%)** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **NO Feature** \\ **Selection** \\ \end{tabular} } & KNN & 77.98 & 73.24 & 72.16 & 83.60 & 95.71 & 80.54 & 0 \\ \hline \multirow{4}{*}{\begin{tabular}{c} **KNN + CFS +** \\ **Heuristic Search** \\ \end{tabular} } & KNN+GA & 77.68 & 71.26 & **78.87** & 84 & 96.54 & 81.67 & 1.40 \\  & KNN+BAT & 77.37 & 72.98 & **78.87** & 84.4 & 96.12 & 81.95 & 1.75 \\  & KNN+PSO & 80.43 & 70.57 & 75.77 & 84 & 96.81 & 81.52 & 1.21 \\  & KNN+FS & 81.04 & 70.40 & 74.23 & 84 & 95.84 & 81.10 & 0.70 \\  & KNN+AS & 78.59 & 71.69 & 75.26 & 82 & 95.29 & 80.57 & 0.03 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **KNN + CFS +** \\ **Exhaustive Search** \\ \end{tabular} } & KNN+GS & 78.90 & 71.34 & 77.32 & 84 & 96.40 & 81.59 & 1.31 \\  & KNN+BF & 80.43 & 70.40 & 75.77 & 82.8 & 96.81 & 81.24 & 0.87 \\ \hline \multirow{4}{*}{\begin{tabular}{c} **KNN +** \\ **Heuristic Search** \\ \end{tabular} } & KNN+GA & 81.35 & 73.49 & 74.74 & 83.6 & 96.26 & 81.89 & 1.68 \\  & KNN+BAT & 79.20 & **73.75** & 77.84 & 84.4 & 96.12 & 82.26 & 2.14 \\  & KNN+PSO & 79.51 & 73.49 & 78.87 & 82.8 & 97.09 & 82.35 & 2.25 \\  & KNN+FS & 80.73 & 73.58 & 75.26 & 82.8 & 96.12 & 81.70 & 1.44 \\  & KNN+AS & 76.15 & 72.81 & 76.29 & 83.6 & 96.40 & 81.05 & 0.63 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **KNN + CNS +** \\ **Exhaustive Search** \\ \end{tabular} } & KNN+GSS & 77.68 & 69.88 & **78.87** & 85.6 & 95.84 & 81.57 & 1.28 \\  & KNN+BF & **85.32** & 73.67 & 77.32 & 84.4 & **97.78** & **83.70** & **3.92** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **KNN + Filter** \\ **Method** \\ \end{tabular} } & KNN+IG & 77.37 & 70.57 & 73.71 & 84.8 & 96.26 & 80.54 & 0.00 \\  & KNN+RF & 81.64 & 73.06 & 68.56 & 84 & 96.12 & 80.68 & 0.17 \\ \cline{1-1}  & KNN+GR & 76.76 & 71.86 & 74.23 & 84.4 & 96.12 & 80.67 & 0.17 \\ \cline{1-1}  & KNN+CV & 77.98 & 69.97 & 74.74 & **86.8** & 95.98 & 81.09 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy Values of K Nearest Neighbor Classifier on Full and Reduced Datasets. observed for CNS, LR with GA search, DT with GSS search, and LR with BF search had accuracy value of 87.16%. LR with GA search had more features (twelve) and LR with BF selected just one feature. However, based on the FFR method, DT with CV based on Ranker search had the highest accuracy value of 87.46% which means the FFR method gave the best performance on CM1 dataset as presented in Table 10. From Table 11, LR with CNS based on AS (77.11%) out-performs all prediction models on the KC1 dataset. The prediction model was built on seventeen features as selected by AS. Other FS methods on KC1 selected smaller features but their respective prediction model had lower accuracy performance.