# Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
## 5 Test Results and Experimental Strategy
All the above explanation is intended to help understand the nature of the performance of both F-scores and the ADDC in our text feature selection, as reported in Table 4. In Table 4, the values of the minimum, maximum, and average ADDC and F-scores are listed in different runs. Table 4 contains five sections; each one represents a particular dataset. The first section lists the results of the \"6 Events Crimes\" dataset that has minimum, maximum, and average scores of F-scores and ADDC. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Qualities**} & \multicolumn{4}{c}{**Minimum**} & \multicolumn{4}{c}{**Maximum**} & \multicolumn{2}{c}{**Mean**} \\ \hline  & **Method** & **F-Macro** & **F-Micro** & **ADDC** & **F-Macro** & **F-Micro** & **ADDC** & **F-Macro** & **F-Micro** & **ADDC** \\ \hline \multirow{8}{*}{6 Events} & All & 0.346 & 0.371 & 0.503 & 0.750 & 0.779 & 0.573 & 0.623 & 0.655 & 0.546 \\  & DE & 0.238 & 0.259 & 0.539 & 0.781 & 0.803 & 0.584 & 0.626 & 0.665 & 0.561 \\  & FSGATC & 0.335 & 0.359 & 0.543 & 0.876 & 0.889 & 0.587 & 0.659 & 0.693 & 0.567 \\  & FSHSTC & 0.371 & 0.388 & 0.525 & 0.655 & 0.701 & 0.558 & 0.542 & 0.582 & 0.544 \\  & DESA & 0.541 & 0.568 & 0.539 & 0.860 & 0.870 & 0.596 & 0.710 & 0.732 & 0.577 \\  & DDESA & 0.573 & 0.608 & 0.554 & 0.887 & 0.897 & 0.629 & 0.761 & 0.779 & 0.491 \\ \hline \multirow{8}{*}{10 Types Crime} & All & 0.416 & 0.484 & 0.524 & 0.735 & 0.766 & 0.627 & 0.617 & 0.661 & 0.595 \\  & DE & 0.462 & 0.510 & 0.509 & 0.691 & 0.732 & 0.610 & 0.553 & 0.587 & 0.583 \\  & FSGATC & 0.399 & 0.476 & 0.470 & 0.599 & 0.676 & 0.791 & 0.473 & 0.533 & 0.776 \\  & FSHSTC & 0.492 & 0.528 & 0.620 & 0.716 & 0.756 & 0.694 & 0.597 & 0.637 & 0.654 \\  & DESA & 0.416 & 0.484 & 0.484 & 0.760 & 0.786 & 0.552 & 0.639 & 0.691 & 0.509 \\  & DDESA & 0.606 & 0.567 & 0.424 & 0.795 & 0.767 & 0.486 & 0.717 & 0.691 & 0.449 \\ \hline \multirow{8}{*}{Pair of 20 news} & All & 0.515 & 0.673 & 0.539 & 0.520 & 0.675 & 0.605 & 0.519 & 0.675 & 0.591 \\  & DE & 0.538 & 0.684 & 0.649 & 0.543 & 0.686 & 0.670 & 0.542 & 0.686 & 0.666 \\  & FSGATC & 0.505 & 0.669 & 0.422 & 0.515 & 0.673 & 0.566 & 0.511 & 0.672 & 0.498 \\  & FSHSTC & 0.524 & 0.678 & 0.685 & 0.529 & 0.680 & 0.720 & 0.528 & 0.679 & 0.710 \\  & DESA & 0.524 & 0.678 & 0.428 & 0.524 & 0.678 & 0.428 & 0.524 & 0.678 & 0.428 \\  & DDESA & 0.560 & 0.675 & 0.304 & 0.560 & 0.675 & 0.304 & 0.560 & 0.675 & 0.304 \\ \hline \multirow{8}{*}{Reuters-21578} & All & 0.177 & 0.194 & 0.403 & 0.559 & 0.616 & 0.536 & 0.280 & 0.308 & 0.476 \\  & DE & 0.205 & 0.212 & 0.395 & 0.345 & 0.366 & 0.477 & 0.260 & 0.283 & 0.438 \\ \cline{1-1}  & FSGATC & 0.181 & 0.196 & 0.450 & 0.426 & 0.467 & 0.493 & 0.290 & 0.320 & 0.468 \\ \cline{1-1}  & FSHSTC & 0.179 & 0.232 & 0.416 & 0.280 & 0.309 & 0.504 & 0.238 & 0.267 & 0.474 \\ \cline{1-1}  & DESA & 0.200 & 0.228 & 0.396 & 0.294 & 0.331 & 0.466 & 0.241 & 0.269 & 0.420 \\ \cline{1-1}  & DDESA & 0.308 & 0.310 & 0.157 & 0.398 & 0.387 & 0.181 & 0.355 & 0.334 & 0.163 \\ \hline \multirow{8}{*}{20 news Groups} & All & 0.115 & 0.145 & 0.413 & 0.386 & 0.424 & 0.559 & 0.261 & 0.296 & 0.499 \\ \cline{1-1}  & DE & 0.104 & 0.135 & 0.434 & 0.307 & 0.326 & 0.526 & 0.196 & 0.229 & 0.477 \\ \cline{1-1}  & FSGATC & 0.104 & 0.135 & 0.363 & 0.383 & 0.410 & 0.606 & 0.220 & 0.241 & 0.454 \\ \cline{1-1}  & FSHSTC & 0.143 & 0.160 & 0.501 & 0.334 & 0.349 & 0.609 & 0.215 & 0.237 & 0.549 \\ \cline{1-1}  & DESA & 0.115 & 0.140 & 0.476 & 0.425 & 0.453 & 0.560 & 0.214 & 0.255 & 0.528 \\ \cline{1-1}  & DDESA & 0.235 & 0.225 & 0.308 & 0.392 & 0.406 & 0.540 & 0.340 & 0.324 & 0.386 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Internal and external evaluation measure. It can be seen that the results of the average of all runs for the proposed DDESA method are 0.573 and 0.608 for minimum, 0.886 and 0.897 for maximum, and 0.761 and 0.778 for average values. All these scores are higher than the other competent methods, including \"All\", which uses the entire feature space.