# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### External Evaluation  
This section compares the classification results obtained by the feature sets generated by \(\text{BBA}_{\text{CO}}\) and \(\text{BBA}_{\text{LHS}}\) for the Reuters and WebKB datasets. In order to compare the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Reuters**} & \multicolumn{2}{c}{**WebKB**} \\ \hline
**Method** & \(\text{BBA}_{\text{CO}}\) & \(\text{BBA}_{\text{LHS}}\) & \(\text{BBA}_{\text{CO}}\) & \(\text{BBA}_{\text{LHS}}\) \\ \hline \multicolumn{5}{c}{generation 80} \\ \hline Mean & 78.07 & 68.95 & 75.12 & 79.15 \\ \(p\)-Value & 0.17 & 0.32 & 0.07 & 0.16 \\ \(t\) State & 6.88 & & \(-2.00\) & \\ \(t\) Critical two-tail & 2.02 & & 2.00 & \\ \hline \multicolumn{5}{c}{generation 100} \\ \hline Mean & 74.52 & 62.95 & 70.85 & 77.64 \\ \(p\)-Value & 0.24 & 0.14 & 0.21 & 0.08 \\ \(t\) State & 5.64 & & \(-2.84\) & \\ \(t\) Critical two-tail & 2.00 & & 2.02 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(Cont.\) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Name** & **Quality of Final Solution** & **\# of the Selected Features** & **Reduction Rate (\%)** \\ \hline Earn & 0.87 & 307 & 82.87 \\ Acquisition & 0.88 & 394 & 84.76 \\ Trade & 0.90 & 103 & 89.24 \\ Ship & 0.88 & 56 & 84.09 \\ Grain & 0.73 & 26 & 82.31 \\ Crude & 0.75 & 131 & 85.11 \\ Interest & 0.83 & 45 & 88.64 \\ Money-fx & 0.76 & 78 & 87.62 \\ Corn & 0.74 & 89 & 88.35 \\ Wheat & 0.79 & 157 & 81.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quality of the final solution, number of features and reduction rate of \(\text{BBA}_{\text{CO}}\) in the Reuters dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Name** & **Quality of Final Solution** & **\# of the Selected Features** & **Reduction Rate (\%)** \\ \hline Student & 0.81 & 226 & 90.23 \\ Faculty & 0.87 & 179 & 93.39 \\ Course & 0.74 & 105 & 94.41 \\ Project & 0.71 & 103 & 93.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Quality of the final solution, number of features and reduction rate of \(\text{BBA}_{\text{CO}}\) in WebKB dataset. classification performance using BBA-based methods, the classification is also conducted using Chi-Square (CHI), Information Gain (IG) and Gini Index (GI) feature-selection methods, which were successfully used in the literature for TFS [74; 75; 76]. Micro Average F1 and Macro Average F1 are used as performance measures for text classification. Some of the best classification results are shown in Table 10 comparing BBA\({}_{\text{CO}}\) with BBA\({}_{\text{LHS}}\), CHI, IG and GI. Table 10 shows the classification results of the Reuters and WebKB datasets using three classifiers (i.e., NB, SVM and KNN) in Micro Average F1 and Macro Average F1. The results clearly show the improvement in the classification performance with all classifiers when using BBA-based TFS methods. In addition, BBA\({}_{\text{CO}}\) improves the classification performance over BBA\({}_{\text{LHS}}\) as a result of the improved feature sets selected by BBA\({}_{\text{CO}}\). Tables 11-14 show the classification results in terms of Precision, Recall and F-measure for the Reuters and WebKB datasets using BBA\({}_{\text{LHS}}\) and BBA\({}_{\text{CO}}\), respectively.