# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### Results on Non-English Datasets
#### 5.3.2 Results of Arabic Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Class} & \multicolumn{3}{c}{NB-No FS} & \multicolumn{3}{c}{SVM-No FS} & \multicolumn{3}{c}{KNN-No FS} & \multicolumn{3}{c}{NB-Chi-Square} & \multicolumn{3}{c}{SVM-Chi-Square} & \multicolumn{3}{c}{KNN-Chi-Square} \\ \cline{2-19}  & P & R & F & P & R & F & P & R & F & P & R & F & P & R & F \\ \hline Addin & 0.93 & 0.57 & 0.70 & 0.80 & 0.57 & 0.66 & 0.45 & 0.69 & 0.55 & 0.98 & 0.73 & 0.84 & 0.71 & 0.96 & 0.82 & 0.94 & 0.76 & 0.84 \\ Bisnes & 0.62 & 0.76 & 0.69 & 0.80 & 0.55 & 0.65 & 0.86 & 0.50 & 0.63 & 0.68 & 0.78 & 0.73 & 0.88 & 0.65 & 0.75 & 0.84 & 0.64 & 0.73 \\ Dekotaman & 0.88 & 0.51 & 0.64 & 0.77 & 0.53 & 0.62 & 0.93 & 0.53 & 0.68 & 0.98 & 0.68 & 0.80 & 0.65 & 0.88 & 0.75 & 0.66 & 0.73 & 0.70 \\ Global & 0.93 & 0.50 & 0.65 & 0.65 & 0.64 & 0.64 & 0.95 & 0.52 & 0.67 & 0.81 & 0.74 & 0.77 & 0.73 & 0.81 & 0.77 & 0.87 & 0.66 & 0.75 \\ Hiburan & 0.91 & 0.50 & 0.64 & 0.76 & 0.52 & 0.62 & 0.93 & 0.51 & 0.66 & 0.90 & 0.69 & 0.78 & 0.90 & 0.58 & 0.70 & 0.69 & 0.85 & 0.76 \\ Pendidikan & 0.86 & 0.60 & 0.71 & 0.74 & 0.66 & 0.69 & 0.88 & 0.63 & 0.73 & 0.65 & 0.73 & 0.69 & 0.76 & 0.65 & 0.70 & 0.78 & 0.69 & 0.73 \\ Santiai & 0.81 & 0.51 & 0.63 & 0.56 & 0.55 & 0.55 & 0.86 & 0.54 & 0.66 & 0.75 & 0.86 & 0.80 & 0.66 & 0.71 & 0.68 & 0.62 & 0.82 & 0.71 \\ Sihat & 0.87 & 0.57 & 0.69 & 0.54 & 0.79 & 0.64 & 0.42 & 0.79 & 0.54 & 0.74 & 0.62 & 0.68 & 0.75 & 0.87 & 0.87 & 0.81 & 0.87 & 0.76 & 0.81 \\ Sukan & 0.59 & 0.98 & 0.74 & 0.66 & 0.91 & 0.77 & 0.59 & 0.97 & 0.73 & 0.56 & 0.72 & 0.63 & 0.97 & 0.76 & 0.85 & 0.74 & 0.99 & 0.85 \\ Teknologi & 0.78 & 0.64 & 0.70 & 0.85 & 0.69 & 0.78 & 0.66 & 0.71 & 0.98 & 0.68 & 0.81 & 0.91 & 0.73 & 0.81 & 0.88 & 0.69 & 0.77 \\ Vroom & 0.63 & 0.63 & 0.63 & 0.89 & 0.63 & 0.74 & 0.91 & 0.55 & 0.69 & 0.98 & 0.76 & 0.85 & 0.93 & 0.77 & 0.84 & 0.86 & 0.59 & 0.70 \\ Average & 0.80 & 0.62 & 0.67 & 0.73 & 0.63 & 0.66 & 0.78 & 0.63 & 0.66 & 0.82 & 0.73 & 0.76 & 0.80 & 0.76 & 0.77 & 0.80 & 0.74 & 0.76 \\ \hline \hline \multicolumn{19}{c}{} \\ \multirow{2}{*}{Class} & \multirow{2}{*}{P} & R & F & P & R & F & P & R & F & P & R & F & P & R & F & P & R & F \\ \cline{2-19}  & & & & & & & & & & & & & & & & & & & & & \\ \hline Addition & 0.84 & 0.77 & 0.80 & 0.77 & 0.86 & 0.81 & 0.82 & 0.76 & 0.79 & 0.71 & 0.95 & 0.81 & 0.90 & 0.82 & 0.86 & 0.86 & 0.84 & 0.85 \\ Bisnes & 0.77 & 0.94 & 0.85 & 0.87 & 0.77 & 0.82 & 0.85 & 0.76 & 0.80 & 0.89 & 0.73 & 0.80 & 0.82 & 0.78 & 0.80 & 0.84 & 0.79 & 0.82 \\ Dekotaman & 0.92 & 0.76 & 0.83 & 0.91 & 0.81 & 0.86 & 0.94 & 0.81 & 0.87 & 0.89 & 0.92 & 0.90 & 0.75 & 0.86 & 0.80 & 0.83 & 0.87 & 0.85 \\ Global & 0.78 & 0.88 & 0.82 & 0.72 & 0.86 & 0.78 & 0.75 & 0.85 & 0.80 & 0.85 & 0.71 & 0.77 & 0.84 & 0.77 & 0.80 & 0.79 & 0.84 & 0.81 \\ H\(\ddot{\text{u}}\)huran & 0.73 & 0.79 & 0.76 & 0.83 & 0.73 & 0.78 & 0.71 & 0.80 & 0.76 & 0.87 & 0.92 & 0.89 & 0.77 & 0.89 & 0.83 & 0.86 & 0.82 & 0.84 \\ Pendidikan & 0.84 & 0.89 & 0.86 & 0.84 & 0.78 & 0.81 & 0.88 & 0.89 & 0.88 & 0.81 & 0.85 & 0.83 & 0.75 & 0.87 & 0.81 & 0.88 & 0.77 & 0.82 \\ Santiai & 0.71 & 0.77 & 0.74 & 0.74 & 0.81 & 0.78 & 0.71 & 0.85 & 0.77 & 0.93 & 0.77 &The classification results in terms of average precision, average recall and Macro average F1 are reported and compared with the state-of-the-art results in Table 22. The state-of-the-art studies in the TFS field utilized different datasets, classifiers and evaluation measures. However, the experimental setting such as the utilized database, classifiers and evaluation metrics should be same in order to reach fair comparison. Thus, few studies are comparable with the proposed method for using the same experimental setting. The methods used for comparison are:  
1. Binary Particle Swarm Optimization with k-nearest neighbor (BPSO-KNN) [78];
2. Enhanced Genetic Algorithm (EGA) [69];
3. Category relevant feature measure (CRFM) [79]. The authors of [78] combined Binary PSO and k-nearest neighbor to select a feature set for Arabic text classification.