# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 4 Experimental Setup
### Evaluation Metric
#### 4.3.1. Internal Evaluation Metric  
For the internal evaluation of the text-feature selection method presented in this work, different evaluation metrics will be used. These metrics are:  
Solution Quality  
This is also called the fitness value or the objective value of the solution. This value is calculated using the adapted dependency degree measure of RST and the number of features. The following equation is used to evaluate each candidate solution:  
\[\textit{Fitness}(x_{i})=p\times\textit{dep}(x_{i})+(1-p)\times\textit{(1/size }(x_{i}))\]  
where \(x_{i}\) is the feature subset found by solution \(i\). _Fitness_ is calculated based on both the dependency measure of rough set theory (\(\textit{dep}(x_{i})\)), and the length of the feature subset (\(\textit{size}(x_{i})\)). \(p\) is a parameter that controls the relative weight of dependency value and feature subset length, where \(p\in[0,1]\). This formula denotes that the dependency value and feature subset size have a different effect on the evaluation. In this study, the dependency value is considered to be more important than the subset length, so \(p\) is set to 0.8, as in [69,70]. Size of the Selected Feature Set  
This metric evaluated the reduction ability of the text-feature selection method. A good method should be able to produce a high-quality feature set with a smaller number of features. The number of features in the resulting feature set is compared with the original number of features before the feature selection process to evaluate the reduction rate. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Sukan** & **Bisnes** & **Pendidikan** & **Sains-Teknologi** & **Hiburan** & **Politik** & **Total** \\ \hline utusan online & 170 & 238 & 300 & 220 & 1030 & 0 & 1958 \\ mstar & 3392 & 10 & 0 & 0 & 4533 & 0 & 7935 \\ astroawani & 96 & 96 & 0 & 96 & 96 & 96 & 480 \\ bharian & 527 & 1292 & 25 & 0 & 27 & 25 & 1896 \\ Total & 4185 & 1636 & 325 & 316 & 5686 & 121 & 12,269 \\ \hline \end{tabular}
\end{table} 
Table 2: Number of collected documents from each website for the classes of Mix-DS. This metric is also used to evaluate the reduction ability of the TFS method in percentage. The reduction rate is calculated based on the number of original features and the number of selected features as:  
\[Reduction\ rate\ (\%)=100\times\frac{\#original\ features-\#selected\ features}{\#original\ features}\]  
The population's diversity of different generations in the algorithm (i.e., generations 1, 20, 50, 80 and 100) is measured graphically to evaluate the exploration ability of the algorithm during the search process and its ability to maintain the population's diversity to later generations. Convergence Behavior  
The convergence of the population is shown graphically to evaluate the ability of the method to keep improving the population and avoid premature convergence. The convergence of the whole population is shown in average, and the convergence of five randomly selected solutions is also shown to show their improvement progress during the search process. Statistical Tests  
The significance test (the \(t\)-test: two-sample assuming unequal variances) is conducted as a statistical analysis to compare the algorithms. The \(t\)-test is a statistical check of two population means. The \(t\)-test was successfully used for comparing two groups of results over multiple datasets for its simplicity, safety and robust results [71]. To perform the \(t\)-test, the \(t\)_State_ and \(t\)_Critical two tail_ values are calculated by Microsoft Excel software. The first group of results is considered significantly higher than the second group if the \(t\)_State_ value is greater than the \(t\)_Critical two tail_. The second group of results is considered significantly higher than the first group if the \(t\)_State_ value is less than -\(t\)_Critical two tail_. The difference between the two groups' results is considered not significant if the \(t\)_State_ value is in the interval [\(-t\)_Critical two tail_, \(t\)_Critical two tail_].