# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### Results on Non-English Datasets
#### 5.3.1 Results of Malay Datasets  
The classification results of Malay datasets utilizing three classifiers and the two versions of the proposed method are reported in this subsection. The results are also compared with the classification results using no feature selection method and using Chi-Square feature selection method. Chi-Square is a well-known method that has been successfully utilized for feature selection [9; 74; 77]. Firstly, the characteristics of the resulting feature sets by BBA\({}_{\text{LHS}}\) and BBA\({}_{\text{CO}}\) are displayed in Tables 16 and 17 for Mix-DS and Harian Metro dataset, respectively. Then, the classification results are reported in Table 18. \begin{table}
\begin{tabular}{c 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 Bisnes & 0.55 & 0.63 & 4785 & 1058 & 1014 & 77.89 & 78.81 \\ Hiburan & 0.56 & 0.60 & 10,357 & 1468 & 1328 & 85.83 & 87.18 \\ Pendidikan & 0.63 & 0.71 & 2212 & 564 & 428 & 74.50 & 80.65 \\ Politik & 0.54 & 0.54 & 1534 & 341 & 325 & 77.77 & 78.81 \\ Sains-Teknologi & 0.59 & 0.65 & 3023 & 784 & 751 & 74.07 & 75.16 \\ Sukan & 0.61 & 0.67 & 8320 & 1127 & 946 & 86.45 & 88.63 \\ \hline \end{tabular}
\end{table} 
Table 16: Characteristics of the selected feature sets in Mix-DS, where Q indicates the quality of feature set, #F indicates to number of features, R indicates reduction rate (%). \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Reuters** & \multicolumn{2}{c}{**WebKB**} \\ \hline
**Method** & **BBA\({}_{\text{LHS}}\)** & **BBA\({}_{\text{CO}}\)** & **BBA\({}_{\text{LHS}}\)** & **BBA\({}_{\text{CO}}\)** \\ \hline  & & NB & & NB & \\ \hline Mean & 92.25 & 93.41 & 91.35 & 92.22 \\ \(p\)-Value & 0.08 & 0.18 & 0.49 & 0.33 \\ \(t\) State & \(-8.23\) & & \(-5.83\) & \\ \(t\) Critical two-tail & & 2.02 & & 2.00 & \\ \hline  & & KNN & & KNN & \\ \hline Mean & 92.17 & 93.09 & 90.59 & 91.88 \\ \(p\)-Value & 0.10 & 0.59 & 0.62 & 0.99 \\ \(t\) State & \(-6.37\) & & & \(-9.74\) & \\ \(t\) Critical two-tail & & 2.00 & & 2.02 & \\ \hline  & SVM & & & SVM & \\ \hline Mean & 91.83 & 93.67 & 91.20 & 92.14 \\ \(p\)-Value & 0.56 & 0.59 & 0.42 & 0.98 \\ \(t\) State & \(-16.54\) & & & \(-7.41\) & \\ \(t\) Critical two-tail & & 2.00 & & 2.03 & \\ \hline \end{tabular}
\end{table} 
Table 15: The \(t\)-test of classification results of BBA\({}_{\text{CO}}\) vs. BBA\({}_{\text{LHS}}\) methods. Tables 16 and 17 compare the performance of BBA\({}_{\text{LHS}}\) and BBA\({}_{\text{CO}}\) in terms of quality, number of selected features and reduction rate. The tables show that both methods are able to reduce the dimensionality of the feature space efficiently. In addition, it is clear that BBA\({}_{\text{CO}}\) outperforms BBA\({}_{\text{LHS}}\) in terms of the quality of the feature set and the reduction rate. Table 18 reports the classification results using the selected feature sets by BBA\({}_{\text{CO}}\) compared with the selected sets by Chi-square, BBA\({}_{\text{LHS}}\) and with the original feature set. The impact of using an efficient feature selection method is clear, which is consistent with what has been concluded in the literature. It is clear how Chi-square has improved the classification results over the original dataset. However, the classification performance with BBA\({}_{\text{LHS}}\) and BBA\({}_{\text{CO}}\) is clearly improved over Chi-square. The results demonstrate the ability of BBA-based methods to select discriminative feature sets. Furthermore, using a coevolutionary technique has clearly improved the performance of the TFS method, which has been reflected in the improved classification results. Tables 19 and 20 show the classification results in terms of precision, recall and F-measure for Mix-DS and Harian Metro dataset, respectively.