# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### External Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Metric** & **Classifier** & **CHI** & **IG** & **GI** & **BBA\({}_{\text{LHS}}\)** & **BBA\({}_{\text{CO}}\)** \\ \hline \multirow{4}{*}{Reuters} & Micro & NB & 88.80 & 90.80 & 90.50 & 92.78 & 93.76 \\  & average F1 & SVM & 86.70 & 89.40 & 89.80 & 92.55 & 94.08 \\  & KNN & 86.30 & 89.90 & 90.30 & 92.63 & 93.17 \\ \cline{2-8}  & Macro & NB & 79.50 & 78.50 & 77.20 & 89.87 & 90.03 \\  & average F1 & SVM & 81.70 & 77.20 & 75.90 & 88.76 & 90.05 \\  & KNN & 66.60 & 68.30 & 69.10 & 88.04 & 89.49 \\ \hline \multirow{4}{*}{WebKB} & Micro & NB & 79.50 & 78.20 & 77.50 & 91.79 & 92.72 \\  & average F1 & SVM & 88.30 & 89.30 & 89.10 & 91.64 & 92.84 \\  & KNN & 65.30 & 66.70 & 65.70 & 90.94 & 92.06 \\ \cline{2-8}  & Macro & NB & 78.20 & 76.90 & 75.90 & 89.82 & 91.67 \\ \cline{2-8}  & average F1 & SVM & 87.00 & 87.90 & 87.80 & 89.37 & 90.51 \\ \cline{2-8}  & KNN & 60.70 & 62.50 & 61.40 & 88.03 & 90.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification results using the feature sets generated by BBA\({}_{\text{LHS}}\) and BBA\({}_{\text{CO}}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**NB**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**KNN**} \\ \cline{2-9}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Earn & 99.18 & 98.60 & 98.89 & 98.09 & 95.35 & 96.70 & 94.02 & 92.25 & 93.13 \\ Acquisition & 93.16 & 98.60 & 95.80 & 85.95 & 96.78 & 91.04 & 90.54 & 82.68 & 86.43 \\ Trade & 96.36 & 91.98 & 94.12 & 97.05 & 87.97 & 92.29 & 87.17 & 92.04 & 89.54 \\ Ship & 97.56 & 78.43 & 86.96 & 96.76 & 75.29 & 84.69 & 82.25 & 88.39 & 85.21 \\ Grain & 91.96 & 71.53 & 80.47 & 94.20 & 78.14 & 85.42 & 81.02 & 89.03 & 84.84 \\ Crude & 81.70 & 94.58 & 87.67 & 87.76 & 82.41 & 85.00 & 97.79 & 91.19 & 94.37 \\ Interest & 95.08 & 94.79 & 94.93 & 96.46 & 88.02 & 92.05 & 82.32 & 87.12 & 84.65 \\ Money-fx & 97.37 & 79.20 & 87.35 & 91.10 & 81.99 & 86.31 & 89.20 & 98.67 & 93.70 \\ Corn & 89.63 & 82.59 & 85.97 & 90.15 & 86.23 & 88.15 & 87.87 & 78.35 & 82.84 \\ Wheat & 88.76 & 84.50 & 86.58 & 88.63 & 83.39 & 85.93 & 89.67 & 82.12 & 85.73 \\ \hline Average & 93.08 & 87.48 & 89.87 & 92.61 & 85.56 & 88.76 & 88.18 & 88.18 & 88.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Precision (P), Recall (R) and F-measure (F) of each class in the Reuters dataset using the BBA\({}_{\text{LHS}}\). Tables 13 and 14 clearly show the improvement in the classification results when using \(\text{BBA}_{\text{CO}}\) as a TFS method. Comparing the classification results achieved by \(\text{BBA}_{\text{CO}}\) with those of \(\text{BBA}_{\text{LHS}}\) (i.e., Tables 11 and 12), it is found that the average F is improved with the three classifiers in both datasets. From the experimental results, it is shown that dividing the solutions into smaller ones and optimizing each part then improved the resulting final solution, controlled the population's diversity and improved the classification performance as a result of the improved selected feature set. In order to test if the performance of \(\text{BBA}_{\text{CO}}\) is significantly better than that of \(\text{BBA}_{\text{LHS}}\), the _t_-test needs to be applied to their classification results. The results of the statistical test are presented in Table 15.  
As shown in Table 15, the value of _t State_ is less than _-t critical two-tail_ in all cases. These results indicate that the second group of the classification results is significantly higher than the first group (i.e., the classification results obtained by using the selected feature set by \(\text{BBA}_{\text{LHS}}\)). The reason behind that is attributed to the cooperative coevolving strategy that is utilized in \(\text{BBA}_{\text{CO}}\), which improves the performance of the text-feature selection method by dividing the solution into smaller ones with a smaller dimension than the original full solution.