# 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
They used three Arabic datasets and three classifiers to evaluate the performance of their method. A feature selection method based on Enhanced Genetic Algorithm (EGA) was proposed in the study by [69]. Their method was evaluated using three Arabic datasets and two classification algorithms namely NB and AC. The same datasets and classification algorithms were used in the study of [79] which proposed three enhanced feature selection methods. However, the results of CRFM have been used in the comparison as it performs better than the other two methods. In the current study, the classification was performed using NB and SVM that where successfully utilized in previous studies for text classification [69; 78; 79; 80]. Table 22 reports the classification results of the two variants of BBA-based TFS methods compared with three state-of-the-art results. Based on Table 22, it is obvious that the results of NB with BBAco outperform the results of all other methods. On the other hand, the results of SVM with BPSO-KNN outperform the results of the other methods. However, unlike BPSO-KNN, the proposed BBA-based methods likely perform above 90% with both utilized classifiers. Hence, it is clear that the proposed BBA-based TFS methods are efficient with the Arabic dataset. Table 23 shows the classification results of the Al-Jazeera news dataset with BBALHS and BBAco in terms of precision, recall and F-measure. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Q-BBA\({}_{\text{LHS}}\)** & **Q-BBA\({}_{\text{CO}}\)** & **\#F** & **\#F-BBA\({}_{\text{LHS}}\)** & **\#F-BBA\({}_{\text{CO}}\)** & **R-BBA\({}_{\text{LHS}}\)** & **R-BBA\({}_{\text{CO}}\)** \\ \hline Economy & 0.54 & 0.61 & 1541 & 537 & 397 & 65.15 & 74.24 \\ Politics & 0.59 & 0.64 & 1427 & 343 & 152 & 75.96 & 89.35 \\ Sport & 0.62 & 0.68 & 1874 & 294 & 199 & 84.31 & 89.38 \\ Science & 0.63 & 0.68 & 1505 & 235 & 176 & 84.39 & 88.31 \\ Art & 0.66 & 0.71 & 1693 & 544 & 267 & 67.87 & 84.23 \\ \hline \end{tabular}
\end{table} 
Table 21: Characteristics of the selected feature sets in Al-Jazeera news dataset, where Q indicates the quality of feature set, #F indicates number of features, R indicates reduction rate (%). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Metric** & **Classifier** & **BPSO-KNN** & **EGA** & **CRFM** & **BBA\({}_{\text{LHS}}\)** & **BBA\({}_{\text{CO}}\)** \\ \hline Macro Average & NB & 85.76 & 91 & 88.77 & 91.34 & 91.64 \\ precision & SVM & 93.7 & - & - & 90.86 & 92.22 \\ \hline \multirow{2}{*}{Macro Average Recall} & NB & 84.34 & 90.66 & 88.33 & 90.42 & 91.09 \\  & SVM & 92.98 & - & - & 91.58 & 92.88 \\ \hline \multirow{2}{*}{Macro Average F1} & NB & 84.63 & 90.83 & 88.55 & 90.73 & 91.24 \\  & SVM & 93.12 & - & - & 91.07 & 92.42 \\ \hline \end{tabular}
\end{table} 
Table 22: Classification results of BBA\({}_{\text{R}}\), BBA\({}_{\text{LHS}}\), and BBA\({}_{\text{CO}}\) on Al-Jazeera news dataset compared with state-of-the-art results. Table 23 shows that the average precision, recall and F-measure with BBA\({}_{\text{CO}}\) are clearly higher than those of BBA\({}_{\text{LHS}}\) in terms of average precision, recall and F-measure in all cases. These results demonstrate the efficiency of the proposed TFS method with the Arabic dataset. This successfully shows that the proposed BBA-based text-feature selection method is applicable with the Arabic language. In order to test whether the improvement of BBA\({}_{\text{CO}}\) over BBA\({}_{\text{LHS}}\) is significant or not, the statistical test (i.e., the \(t\)-test) is performed to the classification results of both methods. The statistical test compares the results of BBA\({}_{\text{LHS}}\) with those of BBA\({}_{\text{CO}}\) using NB and SVM classifiers. The \(t\)-test is conducted for the results of BBA\({}_{\text{LHS}}\) vs. BBA\({}_{\text{CO}}\), to test whether the difference is significant or not. The results of the \(t\)-test are reported in Table 24.  
As shown in Table 24, the value of \(t\)_State_ is less than -\(t\)_Critical two-tail_ with both classifiers.