## 7 Analysis and Discussion of Results of Computer Experiments
### Comparative Analysis of Different Types of Features  
Figure 5 shows the recognition results obtained with different feature types: from left to right, the first column shows the text set on which accuracy is calculated, the second shows feature types, and the third column shows the recognition accuracies. From Figure 5, it can be seen that features belonging to the character n-gram frequency type and features belonging to the word frequency type were more effective in recognition than features belonging to other feature types. Mixed feature groups, in which features belonging to character n-gram frequency and character n-gram frequency variance types were used together, did not improve the recognition accuracy obtained with feature groups consisting of features belonging to only one of these types. Mixed feature groups that shared features belonging to the sentence length frequency and word length frequency types resulted in higher recognition accuracy than feature groups consisting of features belonging to only one of these types, i.e., using sentence length frequencies and word length frequencies together in a mixed feature group was more effective in recognition. Figure 4: Recognition accuracies obtained with word frequencies (%). Table 4 shows the comprehensive recognition results on different text sets and with different confidence percentages of two feature groups with the highest recognition accuracy on Dataset-0 in the author recognition problem considered in the study. One of these two feature groups contains the character n-gram frequencies, and another contains word frequencies. It is clear from the results in the table that these two feature groups caused the same accuracies in Dataset-0 (see \"Works\" in Table 4), but it seems recognition is performed with more confidence with character n-gram frequencies (e.g., see \"Test 75\"--on the test set with 75% confidence). These two cases differ not only because of obtaining different recognition accuracies with certain degrees of confidence but also because of recognition indicators for different author candidates. For these two cases, it seems from the confusion matrices [33] given in Figures 6 and 7 that they were correspondingly obtained on Dataset-0 and the test set. This can be seen from the precision and recall values on Dataset-0 for different author candidates of these two cases given in Tables 5 and 6 as well. The overall precision, recall, F1 score, and Cohen's kappa [36] values obtained on different text sets for those two cases are given in Table 7. It should be noted that the values of precision, recall, and F1 score in Table 7 are the average of per-class precision, recall, and F1 score indicators for author candidates weighted according to the number of texts of those author candidates in the text set. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature Type** & **Number of Features** &
\begin{tabular}{c} **Recognition** \\ **Method Type** \\ \end{tabular} & **Train** & **Test** & **Test 50** & **Test 75** & **Test 95** & **Test 99** & **Works** \\ \hline n-gram frequencies & 50 & SVM & 99.38 & 91.03 & 76.92 & 51.28 & 5.13 & 0.00 & 98.01 \\ word frequencies & 650 & RF & 100.00 & 93.59 & 0.00 & 0.00 & 0.00 & 0.00 & 98.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum recognition accuracies of two best-performing feature groups (%). Figure 5: Maximum recognition accuracies obtained using different types of features. Figure 6: Confusion matrices obtained with two best-performing feature groups on the literary works: (a) n-gram frequencies; (b) word frequencies. Figure 7: Confusion matrices obtained with two best-performing feature groups on the test set: (a) n-gram frequencies; (b) word frequencies.