## 4 Results
### Performance Evaluation
#### 4.1.2 Feature Selection and Dimensionality Reduction  
There are many techniques for improving the classifier's performance. Feature selection (FS) and Dimensionality reduction (DR) are two commonly used techniques that improve classification accuracy [48]. The main idea behind _FS_ is to remove redundant or irrelevant features that are not useful for the classifier [49]. The advantage of _FS_ is that no information about the importance of single features is lost. With _DR_ the size of the feature space is decreased, but without losing vital information [50]. _FS_ methods are usually categorized in two basic methods: wrappers and filters [51]. Wrapper _FS_ methods evaluate multiple models with different subsets of input features and select those features that result in the best performing model according to a performance metric. The number of possible results will increase geometrically as the number of features increases. Filter _FS_ methods use statistical techniques to evaluate the relationship between each input variable and the target variable, and these scores are used as the basis to choose (filter) those input variables that will be used in the model. Filters are either global or  
Figure 3: Accuracy performance (Kendall \(\uparrow\)) compared with related worklocal. Global methods assign a single score to a feature regardless of the number of classes while local methods assign several scores, as every feature in every class has a score [52]. Global methods typically calculate the score for every feature and then choose the top-\(N\) features as the feature set, where \(N\) is usually determined empirically. Local methods are similar but require converting a feature's single score before choosing the top-\(N\) features. Wrappers require much more computation time than filters, and may work only with a specific classifier [51].Filters are the most common \(FS\) method for text classification. Some commonly used \(FS\) methods are a. Recursive Feature Elimination Cross Validation (RFECV) that belongs to the Wrappers methods, b. the information gain (IG) [53] that belongs to filter global \(FS\) methods, and c. the Chi-square (CHI) [54], that belongs to the filter local methods. All these \(FS\) methods are language-independent feature selection methods that produce better accuracy. In these experiments \(RFECV\) is tested using Support Vector Machines (SVM) with linear kernel and the number of cross validation folds is set to 10. Information gain is often applied to find out how well each single feature \(A\) separates the given feature data set \(S\) and it is calculated as follows:  
\[IG(S,A)=I(S)-\sum_{n\in A}=\frac{|S_{n}|}{|S|}I(S_{n}), \tag{6}\]  
where \(n\) is the value of every feature (\(A\)) and \(S_{v}\) is the set of instances where \(A\) has value \(n\). CHI is a supervised \(FS\) method that calculates the correlation of a feature value \(n\) with the class \(m\), and it calculated as follows:  
\[x^{2}=\sum_{i=1}^{n}i\sum_{j=1}^{m}j=\frac{(O_{ij}-E_{ij})^{2}}{E_{ij}}, \tag{7}\]  
where \(O_{ij}\) is the observed frequency and \(E_{ij}\) is the expected frequency. \(DR\) refers to algorithms and techniques that create new features which are combinations of the old features [54]. The most important \(DR\) technique is principal component analysis (PCA) [55]. PCA is an unsupervised dimensional reduction technique. PCA produces new features from the original features by converting the high dimensional space of the original features to a low dimensional space while keeping linear structure. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data (a default value is 0.95). Attribute noise was filtered by transforming the original into the \(PC\) space, eliminating some of the worst eigenvectors, and then transforming back to the original space. The maximum number of attributes to include in the transformed space was set to 5.