## 8 Discussion and Conclusions  
The article considers classifiers for the identification of the author of a Russian-language text, such as classical ML methods (SVM, LR, NB, DT, RF, KNN), neural networks (CNN, LSTM, BiLSTM, RuBERT, MultiBERT, and fastText), and combinations of neural networks' architectures (CNN + LSTM, LSTM + CNN). To train the models, two own datasets containing voluminous works of Russian classics and short comments from VK users were used. The methods implemented in the work show results comparable and superior to those obtained by other researchers. For the classical methods of machine learning, the classification was carried out both on the formed feature space of the text and using TF-IDF. In both cases, the classification of short texts fails to achieve results comparable with deep neural networks. For all-number of disputed authors, the range of accuracy varies from 2 to 30%. The reason for this fact lies in the insufficient length of monosyllabic statements and sentences needed for the formation of a vector describing writing style. When classifying datasets of 2, 5, and 10 authors of literary texts, the SVM, RF, and KNN methods are not inferior to deep NNs, reaching an accuracy of 97%. The obtained accuracy indicates a sufficient volume of text fragments (15,000 characters) for reliable classification based on the frequencies of character \(n\)-grams, as well as the ability of SVM to work with a large feature space. In order to improve the quality of SVM classification, the selection of informative features using GA was carried out. In the selection process, the task was set to maximize the objective function, defined as SVM's accuracy. From the original set of 1168 features, subsets of 500, 400, 300, 200, 100, and 50 features were selected according to the value of the objective function. This solution allows not only to identify informative features but also to eliminate redundant ones that complicate the classification. Vectors of 50 features do not improve the classification on either of the two datasets. In the case of literary texts, training on subsets of 50 and 100 features improves the result for 2, 5, and 10 authors, and in the case of 20 and 50, there is no increase in accuracy. For both sets of text data, SVM training on the set of 400 features selected by GA allows achieving up to a 10% increase in accuracy for all datasets. This fact makes it possible to improve the learning rate of the process of training, reduce the load on computing resources and eliminate the redundancy of a feature set. Based on the experiments and analysis of works devoted to the selection of informative features, several properties that characterize informative features can be noted:  
1. Unconsciousness. In the case of choosing a feature that is poorly controlled by the author's consciousness, its deliberate distortion becomes less likely. 2. Immutability. The value of the feature is constant within a certain limited range for one author. Such features make it possible to distinguish between two or more authors with a similar writing style or someone, who is trying to imitate the style. Deep neural networks, in contrast with SVM, can automatically identify implicit informative features for classification. Accuracy of more than 98% was obtained when training CNN on the dataset of literary texts. This result is higher than the accuracy of the SVM trained on a selected set of features with the maximum result in the entire series of experiments. The accuracy of LSTM, including bidirectional ones, as well as their combinations with CNNs, achieves high accuracy for all datasets, but their training time in almost all cases exceeds the time spent on training SVM and other classical ML methods. The optimal variant is fastText, the learning rate of which is on average 51% faster than for considered deep NNs, and the accuracy is below the maximum for all models by no more than 3%.