## 5 Characteristics of Feature Groups
### Word Frequency Type of Feature Groups  
In the considered author recognition problem, different feature groups consisting of features related to the type of word frequency were used. These feature groups differ from each other according to the variety and number of words used in these groups, as well as the frequency types, i.e., in addition to the frequencies of the words in the given text, the features of the given text may also include the frequencies of these words in candidate authors' unified texts and the total usage frequencies in the training set (see Section 3.5). We divide these feature groups into several types. It may be confusing, but it is not about the type of features but the type of feature group. We will only separate groups of features of word frequency type into such feature group types due to the large number of approaches used for the selection of words, the values of which, in these groups, are quite different from each other. Let us take a look at the characteristics of each of the different feature group types in the study, which consist of features belonging to the word frequency feature type used:1. With the help of feature selection procedure 1--the procedure for selecting frequently used words by authors (see Formulas (1)-(4))--the most frequently used words of the authors in their own texts in the training set are determined separately per each author. Using procedure 1, all the words in the training set are sorted \(L\) times for \(L\) number of authors each to the order of the decreasing total usage frequency by an appropriate author, which gives \(L\) sets of words. Then, an almost equal number of words is selected from each of the word sets. Finally, selected words are collected together as a final set of words selected with feature selection procedure 1. In the study, 15, 25, 45, and 50 high frequent and middle frequent words of authors are used as feature groups. We denote these feature groups by the names W_p1 high15, \(\ldots\), W_p1 high50, W_p1 middle15, \(\ldots\), W_p1 middle50, where \"W\" is \"word\", \"p1\" indicates that the first feature selection procedure is used, \"high\" and \"middle\" indicates that the most and middle frequently used words by the authors are selected, and 15, \(\ldots\), 50 indicate counts of features in these groups. 2. Words that are rarely used in everyday life can be more effective in author recognition in terms of discriminating texts of different authors than words that are often used in daily life. Taking this into account, among the most used words by the authors individually, the frequencies of which were used in groups W_p1 high15, \(\ldots\), W_p1 high50, we manually selected the words that we assume are used less often in everyday life and manually selected the feature groups consisting of the frequencies of these words. In other words, we removed words that are often used in everyday life among the words selected with the help of the first feature selection procedure. The numbers of words in such groups are 3, 15, 25, 40, and 45, and let us denote these groups by the names W_p1 highMAN3, \(\ldots\), W_p1 highMAN45, respectively, where \"MAN\" is for \"manual\", which means that after selecting the most used words by the authors with the help of feature selection procedure 1, some of these words were manually selected from this word set. 3. Using the second feature selection procedure--the procedure for selecting frequently used words in the training set (see Formulas (5)-(7))--5, 10, \(\ldots\), 25, 50 most and middle occurring words in the training set where the authors of the texts are not taken into account were selected and used in feature groups. Let us denote these feature groups as W_p2 high5, \(\ldots\), W_p2 high50, W_p2 middle5, \(\ldots\), W_p2 middle50. 4. In author recognition problems, such text features should be used that represent the stylistic characteristics of its author in any given text of him/her [1].