## 6 Feature Selection Using Genetic Algorithm
The experimental results for short social media texts are presented in Table 11, for literary texts--in Table 12.  
Based on the presented results, the decrease in the number of features by more than half (400) not only does not reduce the classification accuracy but also makes it possible to improve the result on both datasets. The accuracy obtained for 200 features exceeds the original in the case of literary texts and is comparable with original accuracy for short texts. 100 and 50 features are not sufficient to identify the author in all cases. The set of 400 features, on which the maximum accuracy was achieved, contains the frequency distributions of six punctuation marks, eight parts of speech, 165 words from the frequency dictionary, and character-level \(n\)-grams (20 unigrams, 107 bigrams, 98 trigrams). To check if there is a statistically significant difference between results obtained by SVM trained using the different number of informative features selected by the GA, a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{6}{c}{**Number of Features**} \\ \cline{2-7}  & **1168** & **500** & **400** & **300** & **200** & **100** & **50** \\ \hline
2 & 72.2 \(\pm\) 4.0 & 75.3 \(\pm\) 5.2 & **80.3 \(\pm\) 3.3** & 75.2 \(\pm\) 4.7 & 67.2 \(\pm\) 4.5 & 64.9 \(\pm\) 3.8 & 65.3 \(\pm\) 5.5 \\
5 & 69.9 \(\pm\) 3.5 & 70.4 \(\pm\) 2.5 & **77.1 \(\pm\) 2.8** & 72.4 \(\pm\) 1.9 & 63.8 \(\pm\) 3.9 & 59.0 \(\pm\) 4.2 & 49.8 \(\pm\) 3.9 \\
10 & 66.3 \(\pm\) 3.8 & 67.8 \(\pm\) 3.7 & **71.9 \(\pm\) 2.6** & 66.6 \(\pm\) 3.1 & 60.2 \(\pm\) 3.8 & 57.2 \(\pm\) 3.9 & 47.2 \(\pm\) 2.1 \\
20 & 55.4 \(\pm\) 3.1 & 62.4 \(\pm\) 2.9 & **64.8 \(\pm\) 3.0** & 59.3 \(\pm\) 2.8 & 52.9 \(\pm\) 4.1 & 49.4 \(\pm\) 4.2 & 43.7 \(\pm\) 3.7 \\
50 & 32.1 \(\pm\) 3.9 & 35.1 \(\pm\) 6.3 & **37.3 \(\pm\) 4.1** & 33.5 \(\pm\) 2.5 & 27.4 \(\pm\) 4.3 & 26.8 \(\pm\) 3.0 & 22.4 \(\pm\) 4.0 \\ Avg. accuracy & 59.2 \(\pm\) 3.4 & 62.2 \(\pm\) 4.1 & **66.3 \(\pm\) 3.2** & 61.4 \(\pm\) 3.0 & 54.3 \(\pm\) 4.1 & 51.5 \(\pm\) 3.8 & 45.7 \(\pm\) 3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of GA for social media texts dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{6}{c}{**Number of Features**} \\ \cline{2-7}  & **1168** & **500** & **400** & **300** & **200** & **100** & **50** \\ \hline
2 & 95.4 \(\pm\) 1.7 & 96.1 \(\pm\) 2.2 & **98.6 \(\pm\) 2.7** & 94.5 \(\pm\) 1.9 & 96.2 \(\pm\) 1.6 & 98.3 \(\pm\) 3.9 & 95.9 \(\pm\) 3.1 \\
5 & 94.6 \(\pm\) 2.1 & 94.7 \(\pm\) 3.3 & **97.5 \(\pm\) 3.1** & 90.6 \(\pm\) 1.8 & 95.0 \(\pm\) 3.5 & 97.4 \(\pm\) 1.9 & 94.8 \(\pm\) 2.0 \\
10 & 81.9 \(\pm\) 4.6 & 83.7 \(\pm\) 4.1 & **88.0 \(\pm\) 2.9** & 82.1 \(\pm\) 1.2 & 87.1 \(\pm\) 3.4 & 85.9 \(\pm\) 3.8 & 84.3 \(\pm\) 3.2 \\
20 & 63.3 \(\pm\) 4.8 & 69.1 \(\pm\) 2.5 & **73.7 \(\pm\) 3.3** & 70.7 \(\pm\) 2.4 & 72.9 \(\pm\) 2.8 & 63.2 \(\pm\) 2.5 & 60.7 \(\pm\) 2.6 \\
50 & 37.7 \(\pm\) 6.3 & 40.2 \(\pm\) 2.0 & **44.4 \(\pm\) 2.6** & 40.0 \(\pm\) 3.7 & 42.4 \(\pm\) 4.2 & 38.1 \(\pm\) 3.7 & 33.8 \(\pm\) 2.3 \\ Avg. accuracy & 74.7 \(\pm\) 3.9 & 76.8 \(\pm\) 2.9 & **80.4 \(\pm\) 2.9** & 75.6 \(\pm\) 2.2 & 78.3 \(\pm\) 3.1 & 76.6 \(\pm\) 3.2 & 73.9 \(\pm\) 2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of GA for literary texts dataset. rank-based non-parametric Friedman and Nemenyi post-hoc tests were applied to different cross-validation folds results. [PERSON] and Nemenyi tests are suggested to use in the field of ML. These tests were performed for the most difficult of the considered cases--the case of 50 authors. The null hypothesis was that the difference between the results obtained on different numbers of features is only random. An alternative hypothesis was that there is a statistically significant difference between the results. The \(\mathbf{p}\)-value was 0.017 and 0.0007 for literary texts and comments from social networks, respectively. Since these values are less than 0.05, the null hypothesis can be rejected.