**5. Feature Selection**

Feature selection refers to the process of selecting features (=variables) that are relevant for a task and, thus, discarding irrelevant or redundant features from a data set [25–29]. This differentiates feature selection from another dimensionality reduction approach termed feature extraction. Feature extraction transforms the existing features into "new" ones and, subsequently, keeps only some of these new features, whereas feature selection chooses a subset of the original features to retain [30–32]. Using feature selection is generally associated with several advantages and motivations such as (1) improving (or at least not considerably decreasing) the error of the final model [33–37], (2) increasing the speed of model training, and obtaining more simple models from the data [33–36], (3) reducing computational cost and data storage requirements [33–35], and (4) obtaining more easily visualizable and interpretable data [33–35,38,39].

When feature selection is applied in the context of supervised learning, such as classification or regression, it is referred to as supervised feature selection [30,39]. Supervised feature selection can be divided into three types: filter, wrapper, and embedded methods [31,39–41]. Filter methods are part of the pre-processing of the data and only use the characteristics of features to determine their relevance, thus, they do nit involve any learning algorithm (e.g., classifier) [31,39,41,42]. Wrapper methods deploy the learning algorithm as a "blackbox" to evaluate different feature subsets (e.g., using classification

accuracy) and to select the best performing one [39,43–46]. Embedded methods are as wrapper methods classifier-dependent, but unlike wrapper methods, they are part of the model training of the learning algorithm itself [25,33,47,48]. Thus, the feature subset generated by embedded methods can be seen as a byproduct of model training [47].

This research will use commonly known embedded feature selection methods, in particular random forests and support vector machines with recursive feature elimination (RFE), to train the classification models for this study. The software used for coding is Matlab version 2020a.
