## 7 Analysis and Discussion of Results of Computer Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Type**} & \multicolumn{3}{c}{_n=1 Character_} & \multicolumn{3}{c}{_n=2 Character_} \\  & **ANN** & **RF** & **SVM** & **ANN** & **RF** & **SVM** \\ \hline n-gram frequencies & 1.99 & 92.72 & 43.05 & 31.13 & 96.69 & **98.01** \\ variances of n-gram frequencies & 9.93 & 60.93 & 24.50 & 31.13 & 70.20 & 48.34 \\ Mixture of n-gram frequencies and variances of n-gram frequencies & 9.93 & 85.43 & 37.75 & 9.93 & 74.17 & 58.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition accuracies (%) obtained by character n-gram frequencies and variances of character n-gram frequencies. with the highest frequency 5, 10,..., 25, 50, 100 bigrams among 100 bigrams, i.e., first or average in descending order of total frequency in the training set, and the other graph shows the results obtained with the lowest frequency 5, 10,..., 25, 50, 100 bigrams. From the graphs in Figure 1, it can be seen that the frequencies of the most frequently used bigrams in the training set give higher recognition accuracy than the frequencies of the averagely used n-gram characters in the training set. The second conclusion drawn from this figure is that among the first 100 bigrams in the descending order of frequency in the training set, those with lower frequencies in the training set are more effective than those with higher frequencies. This means that in order to use their frequencies in feature groups, it is advised to select character n-grams in the training set that are neither among the most used nor the least used ones in the texts. The conclusions about the recognition accuracies obtained with groups of features of character bigram frequency type in Figure 1 are analogous to the conclusions about the recognition accuracies obtained with groups of  
Figure 1: Recognition accuracies obtained with variances of 2-gram frequencies in feature selection procedure 3: (**a**) frequencies of first bigrams in the descending order of total frequency in the training set; (**b**) frequencies of middle bigrams in the descending order of total frequency in the training set. Figure 2: Recognition accuracies obtained with 2-gram frequencies in feature selection procedure 3: (**a**) variances of frequencies of first bigrams in the descending order of total frequency in the training set; (**b**) variances of frequencies of middle bigrams in the descending order of total frequency in the training set. features of character bigram frequencies variance type in Figure 2. Character bigrams used to obtain results are appropriately the same for these two feature types in Figures 1 and 2. The maximum recognition accuracies obtained on Dataset-0 by various machine learning methods and models--here, just SVM and RF were used with frequencies of bigrams selected using feature selection procedure 4 (the procedure for selecting character n-grams with different characterizing degrees of authors)--are shown in Figure 3. In each chart in Figure 3, there are two graphs. In chart (a), the results were obtained using 4.1 and 4.2 variants, and in chart (b), 4.2 and 4.3 variants of feature selection procedure 4 are represented. For a detailed description of the feature groups using 4.1, 4.2, and 4.3 variants, see Section 5.2. For 4.1-3 variants of the fourth feature selection procedure, see Formulas (12)-(14). For calculating the difference indicator characterizing degrees of authors, these variants use characterizing degrees of best-characterized two, three, and all authors, respectively. It can be seen from Figure 3 that as the number of best-characterized authors taken into account when calculating the difference indicator of characterizing degrees increases, in many cases, more accurate recognition results are obtained. Despite this, the procedure for selecting character n-grams with different characterizing degrees of authors--feature selection procedure 4--did not outperform the procedure for selecting frequently used character n-grams in the training set--feature selection procedure 3.