## 1 Introduction  
In the XXI century, the Internet has become a communication space of the information society. Everyone has an opportunity to express his opinions, share thoughts, and receive feedback from followers. The variety of available online texts (e.g., emails, blogs, correspondence in social networks, and messengers) and possibility to write them anonymously indicate a wide range of applications of methods for determining the author of the text [1]. The number of search results of the query \"Authorship attribution\" in Google Scholar for 2021 is 23,000, for 2017-2021--60,000. These facts indicate the interest of researchers in this issue and characterize this task as relevant. In the regard, the publication of materials on behalf of famous persons from hacked accounts deserves special mention. The text sent by an attacker on behalf of a celebrity is capable to become viral and quotable immediate. Additionally, in the case of the documents containing calls for actions prohibited by law, these publications can negatively influence the public mood. Online media are also actively used by attackers. The fact that usually, users are not required to provide true information about themselves--name, age, gender, and address [2] allows attackers to send and provide anonymous dissemination of antisocial information, threats, and advocacy of terrorism, online fraudulent activities. Establishing the identity of the criminal becomes a non-trivial task, but the authorship attribution methods allow identifying the creator of the text. Nowadays, authorship attribution results are applied in forensic, information security, computational linguistics, and copyright protection. Authorship attribution can be categorized into four groups:  
1. Authorship identification. The task comes down to the multi-class single-labeled text classification problem. In the classic version, the closed attribution problem is considered, that is, the true author of the disputed text is present in a set of candidate authors. For more complicated cases, authorship identification techniques are used to solve open attribution problems. The attribution system should establish the absence or presence of the true author in the list of candidate authors and determine the true author. 2. Authorship verification. The authorship verification task comes down to the problem of one-class classification. The essence of the task is solving the question of whether two documents were written by the same person or not. 3. Authorship clustering. This is the most difficult task when there are many texts and it is necessary to group them by the author, but there is no information about the number of candidate authors. 4. Authorship profiling. Classification by additional author's characteristics such as gender, age, educational level, etc. Novel trends of authorship attribution are associated with the identification of the author of the texts that were distorted by anonymization methods [3], as well as the distinction between natural and generated texts [4]. The study considers the problem of closed attribution. The task is set as follows: there are text fragments belonging to a finite set of authors; the authorship of some fragments is established; other anonymous texts belong to one of the candidates, but whose exactly is unknown. It is necessary to determine the belonging of the disputed fragments to the true author. In this case, the authorship identification problem comes down to the multiclass classification. The set of authors will be a set of classes, and texts whose authors are known will be the training set. The goal is to classify disputed texts with the best accuracy.