## 3 Materials and Methods
### Informative Variable Identifier
In rows are the different ML techniques: covariance multiplication estimator (CME), support vector machine classification (SVC), gradient boosting (GB), linear regression (LR), and linear discriminant analysis (LDA). In columns are the different features, as defined previously. Green stands for scenarios where features were identified as relevant. Red stands for features not identified as relevant during the analysis. As a general result of the analysis, we can recall that although we focused on a limited number of families of ML linear algorithms, each of them treated independently revealed equivalent performance, and the accuracy was tightly related to the features incorporated as input variables. These results indicates two separate, relevant ideas: (i) the importance of FS as a key element for the performance of the ML model; (ii) the limited relationship among accuracy and the ML method that is chosen provides the possibility of picking the method based on its computational efficiency without leaving out any potential expressive capacity. The results obtained in previous experiments suggested the need for a greater and in-depth analysis of both the FS techniques and the variables themselves, for a better understanding of the underlying dynamics. To do so, a number of experiments were conducted considering all variables, for both filters (RFF and MIFF). Experiments were designed in a way to visualize the contribution of each variable by incrementally adding features on a one-by-one basis. Figures 3 and 4 represent the resultsof the corresponding experiments. In the experiments, the variables were added up in sequential order according to their relevance, starting with the most relevant one as defined by the IVI method. Figure 3 presents the evolution of the sequential results for all the experiments when applying the RFF filter, and Figure 4 shows the results of applying the MIFF filtering, both in an M-mode presentation (3D perspective) and in a profile representation. The plots represent two scenarios for each method, as a continuous lines depicts the corresponding results for the standard IVI representation, and the overlaid dotted line follows the process using the RFF or MIFF filtering. IVI standard features were incorporated in the incremental feature experiments in order of relevance (specifically, first informative, then redundant, and finally, noisy). As only filtered features were chosen for RFF and MIFF, the remaining components were added according to standard IVI sequence to complete the full feature set. In Figures 3 and 4, we can observe the convergence in accuracy using the RFF and MIFF filters. We can see in these figures that once we achieved higher accuracy with the relevant features, the accuracy did not experience variations when adding new features. It can be observed that redundant and noisy features were properly classified within the IVI strategy, and no increment in accuracy was obtained when these last (redundant and noisy) variables were added into the model. Meanwhile, in MIFF and RFF experiments, missclassified features may contribute in advance to the sequence to build the model, either delaying the convergence or even limiting the classification power. In our observations, we confirmed that after applying RFF, we did not find a strong limitation in final classification power (see Figure 3 for RFF and Figure 4 for MIFF). Both models sensitively matched the IVI method's accuracy. As a result, a more efficient and faster way to train the final models with a lower number of variables. This effect is used so long as high accuracy is reached consistently in the filtering models.