# Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
## 5 Test Results and Experimental Strategy
The ADDC measure of the average values of the \"6 Events Crimes\" dataset obtained using DESA is slightly higher than the average ADDC obtained using the DESA method. The slight increase of ADDC can be accepted as the corresponding F-scores are much higher than the scores achieved using other methods in terms of their ADDC scores. The second section describes the Classic3; in this section, similar information is shown as in the \"6 Events Crimes\" dataset section, as is listed in the table. Again, it can be seen that the DDESA achieved higher results of clustering in terms of F-scores in comparison to other methods, including the \"All\" method. The ADDC values of the DDESA method also show an improvement by obtaining the least values among other methods. However, the ADDC values are still insignificant when they are compared to other methods. Therefore, the use of external measures will be considered. The \"Pair 20 news Groups\" is a subset of the \"20 news Groups\" dataset used to see the effect of using different methods on the two-class only dataset. The distinction of this dataset and other datasets used is that it has only two classes. Undoubtedly, the lower class number makes it much easier for the clustering algorithm to predict the right class for each document without the confusion of dividing features into multiple classes. Therefore, it can be seen that all feature selection methods tested have almost similar behavior. There are no significant changes in the performance of the DESA method \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dataset** & **Old Features** & **New Features** & **Reduction Rate** \\ \hline \multirow{4}{*}{DE} & 6 Event Crimes & 3863 & 1936 & 0.5 \\ \cline{2-5}  & 10 Types Crime & 362 & 141 & 0.61 \\ \cline{2-5}  & Reuters-21578, & 9496 & 4688 & 0.51 \\ \cline{2-5}  & 20 news Groups & 507 & 189 & 0.63 \\ \cline{2-5}  & Pair 20 news Groups & 15,600 & 3697 & 0.76 \\ \hline \multirow{4}{*}{FSGATC} & 6 Event Crimes & 3863 & 1920 & 0.5 \\ \cline{2-5}  & 10 Types Crime & 362 & 183 & 0.49 \\ \cline{2-5}  & Reuters-21578 & 9496 & 4758 & 0.5 \\ \cline{2-5}  & 20 news Groups & 507 & 235 & 0.54 \\ \cline{2-5}  & Pair 20 news Groups & 15,600 & 3670 & 0.76 \\ \hline \multirow{4}{*}{FSHSTC} & 6 Event Crimes & 3863 & 1924 & 0.5 \\ \cline{2-5}  & 10 Types Crime & 362 & 186 & 0.49 \\ \cline{2-5}  & Reuters-21578 & 9496 & 4770 & 0.5 \\ \cline{2-5}  & 20 news Groups & 507 & 250 & 0.51 \\ \cline{2-5}  & Pair 20 news Groups & 15,600 & 3710 & 0.76 \\ \hline \multirow{4}{*}{DESA} & 6 Event Crimes & 3863 & 1910 & 0.51 \\ \cline{2-5}  & 10 Types Crime & 362 & 108 & 0.7 \\ \cline{2-5}  & Reuters-21578 & 9496 & 4600 & 0.52 \\ \cline{2-5}  & 20 news Groups & 507 & 174 & 0.66 \\ \cline{2-5}  & Pair 20 news Groups & 15,600 & 3653 & 0.77 \\ \hline \multirow{4}{*}{DDESA} & 6 Event Crimes & 3863 & 1892 & 0.51 \\ \cline{2-5}  & 10 Types Crime & 362 & 94 & 0.74 \\ \cline{1-1} \cline{2-5}  & Reuters-21578 & 9496 & 3780 & 0.6 \\ \cline{1-1} \cline{2-5}  & 20 news Groups & 507 & 160 & 0.68 \\ \cline{1-1} \cline{2-5}  & Pair 20 news Groups & 15,600 & 3528 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Reduction rate. when compared with other methods in terms of the external and internal measures. Due to the lower class number, the feature selection does not seem to be playing a notable role. Reuters is one of the widely used benchmark datasets. In this dataset, the values achieved DDESA are comparable with those achieved using the FSGATC method for the maximum. Correspondingly, the ADDC values of the DDESA were less than other methods, including \"All\" with minimum, maximum, and average values. Finally, with \"10 Types of Crimes\", the values of the DDESA are very comparable to other methods in terms of the F-scores and ADDC, including the \"All\" method. Through Table 4, an idea can be concluded that using feature selection improves the performance of internal and external evaluation measures.