## 2 Related Works
### Related Works on Feature Selection  
In most of the analyzed studies, various features were used both in the form of an aggregated vector of all features and as separate sets. The most commonly used features are bigrams and trigrams of symbols and words, functional words, the most frequent words of the language, POS tag frequencies, punctuation marks, distribution of word length, and sentence length. However, not all of them are effective. The set of selected features can be informative, uninformative, and redundant. Uninformative and redundant features are useless for classification. In addition, such features could reduce the effectiveness of classification due to the large dimension of the feature space, called \"the curse of dimensionality\". The purpose of feature selection is to obtain a subset of informative features and exclude uninformative and redundant ones to improve classification accuracy.  
Feature selection for classification is used in many tasks: handwritten signature verification [36]; evaluation biometrics identification ability by the number of participants, their gender, and age [37]; for cardiovascular disease diagnostics [38] and other fields. For forensic purposes, it is important to ensure the quality of the evidence. As such, feature selection is used to choose multiple informative features, and improve the efficiency of author identification. Feature selection is a primary method to provide convincing and reliable evidence to support authorship attribution results.  
Selection method is performed using GAs or metaheuristics. The latter is divided into two main groups: filter approaches and wrapper approaches. The advantages of filter approaches are their independence on learning algorithms, rapidity, and versatility. In addition, wrapper approaches permit the achievement of higher accuracy, but in a more time.  
In the approach proposed by the authors [39], a correlation-based filter feature selection method was used to filter out uninformative features. A particle swarm optimization based (PSO) wrapper method was proposed for selecting informative features after filtering. Experiments were conducted on the two datasets: Blog (12 authors, 200 Chinese texts per author, 583 features) and E-mail (15 authors, 100 English texts per author, 386 features). Both datasets were randomly divided into a training set (70%) and a test set (30%). KNN was used with \(k=5\) and 10-fold cross-validation to evaluate the performance of a selected subset of features. In all PSO-based algorithms, the population size was 30, the maximum number of iterations was 100, the inertia weight \(w\) was 0.7298, and the acceleration constants were 1.49618. The particles in the swarm were initialized randomly. Author's method allowed to increase an accuracy from 45.9% to 76.2% for the Blog dataset, from 56.7% to 81.3% for the Email dataset. For the Blog dataset 93 out of 586 attributes were selected as informative, for Email--25 out of 386.  
In [40] was considered the problem of binary classification. This paper described the application of PSO. PSO selected a specific classifier for each author completely automatically. In addition, PSO also selected preprocessing and feature selection methods. Two datasets were used: MX-PO (353 texts), and CCAT (5000 texts). The best result (96.6% accuracy) was obtained for the author [PERSON]. The NB classifier was built in 104 out of the 3400 features. The lowest accuracy of 14.8% was obtained for the author [PERSON], whose texts were used with normalization for preprocessing and SVM.