## 4 Experimental Analysis and Results
### Hyper-Parameters Initialization  
This subsection explores the hyper-parameters initialization process of metaheuristic-based feature selection and classification modules. The feature selection with a cross-validation module is used to identify a combination of features that gives the best result using the GA technique. The feature selection using the GA-based metaheuristic search selects the features of text after its representation, and the values of its hyper-parameters can control the feature selection process. Moreover, the classification task of multilabel classes is performed by the classification module. These hyper-parameters are variables that determine how the feature selection process and classification task can work. For the classification module, the hyper-parameters of classifier models are initialized with their default values. On the other hand, for initializing the hyper-parameters of metaheuristic-based feature selection with a cross-validation module, the 'Trial and Error' method is applied for tuning with appropriate values and used as default values for the module. The workflow of this method starts after building the classification models. Many possible hyper-parameter values are tried based on our experience and the analysis of previously evaluated experimental results. This method requires an adequate amount of experience and prior knowledge to select the optimal or near-optimal values for the hyper-parameters with restricted time. Cross-validation and feature selection are used to improve the classification performance and avoid overfitting. Table 3 shows the hyper-parameters of feature selection with a cross-validation module. For more details, you can refer to our library at the following link: [[https://pypi.org/project/TextFeatureSelection/](https://pypi.org/project/TextFeatureSelection/)]([https://pypi.org/project/TextFeatureSelection/](https://pypi.org/project/TextFeatureSelection/)) (accessed on 1 June 2023).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyper-Parameter** & **Description** \\ \hline doc\_list & Python list with text documents \\ \hline use\_class\_weight = True & Python list with Y labels \\ \hline save\_data = True & Boolean value representing if you want to apply class \\ \hline label\_list & The list of labels \\ \hline use\_class\_weight = True & The use of class weight \\ \hline n\_crossvalidation = total\_cross\_val & The number of cross-validation \\ validation\_done & \\ \hline n\_crossvalidation = total\_cross & How many cross-validation samples \\ \hline stop\_words & Stop words for count and TF-IDF vectors \\ \hline \hline \end{tabular}
\end{table}
Table 3: The hyper-parameters of feature selection with cross-validation module.  
Moreover, the selected default values of metaheuristic hyper-parameters are given in Table 4. For more details, you can see our library at the following link: [[https://pypi.org/project/TextFeatureSelection](https://pypi.org/project/TextFeatureSelection)]([https://pypi.org/project/TextFeatureSelection](https://pypi.org/project/TextFeatureSelection)) (accessed on 1 June 2023).  
For the genetic algorithm, Table 5 gives the selected default values of its hyper-parameters. The code of the optimization process is available in our library, and for more details, refer to this link: [[https://pypi.org/project/MetaHeuristicsFS/](https://pypi.org/project/MetaHeuristicsFS/)]([https://pypi.org/project/MetaHeuristicsFS/](https://pypi.org/project/MetaHeuristicsFS/)) (accessed on 1 June 2023).