## 3 Materials and Methods
### Informative Variable Identifier
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**method\_Classifier** & **Acc\_all\_features** & **IVI (Relevant)** & **Acc\_fs\_MIFF** & **Acc\_fs\_RFF** \\ \hline GB & \(0.7433\pm 0.0171\) & \(0.7431\pm 0.01911\) & \(0.7510\pm 0.0191\) & \(0.7446\pm 0.0190\) \\ SVC & \(0.7580\pm 0.0170\) & \(0.7598\pm 0.0205\) & \(0.7663\pm 0.0204\) & \(0.7256\pm 0.0228\) \\ LDA & \(0.7533\pm 0.0172\) & \(0.7463\pm 0.0203\) & \(0.7573\pm 0.0193\) & \(0.7443\pm 0.0213\) \\ LR & \(0.7563\pm 0.0174\) & \(0.7576\pm 0.0190\) & \(0.7563\pm 0.0189\) & \(0.7476\pm 0.0209\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results for accuracy for different ML methods on the real dataset. Means and standard deviations of the results for one-hundred-resample analysis. Figure 6: Results of the IVI algorithm for each ML technique on the real dataset. In rows are the different ML techniques: CME, SVC, GB, LR, and LDA. In columns have the different features. Green stands for scenarios where features were identified as relevant. Red means a feature was not identified as relevant during the analysis. Figure 7: Graphical results for accuracy through of incorporation of the features in sequential order of relevance to RFF models, enabling us see the evolution in accuracy for every feature that is added. In 3D plots (**a**), we can see number of features on the x-axis, on the y-axis are the different ML algorithms (GB, SVC, LDA, and LR), and the accuracy is on the z-axis. For each ML algorithm, we present the evolution of features selected in IVI as a continuous line and the features selected by the filter as a dashed line. In (**b**), show a comparative between filters and IVI. For clarity and simplicity we only show one comparative sample. In (**b**), red line represents a dummy classifier that makes predictions with the most frequent class, and the green line dashed is the results of the classifier trained with all features. In the informative representation in Figure 8a,b, we can see an interesting effect. Using RFF, we had an explosive increment in terms of accuracy, while using a relatively small set of features, but we did not achieve the maximum. However, when using MIFF, an initial slight ramp-up was obtained, but we achieved the maximum in accuracy, surpassing IVI's accuracy while using a smaller number of features. This situation was the same in all models, as represented in Figure 9 from a different perspective. In this representation, for the reader's convenience, the high dimensionality of the data is restricted in terms of the

Figure 8: Graphical results for accuracy through of incorporation of features in sequential order of the relevance for MIFF models, enabling us see the evolution in accuracy for every feature that is added. In 3D plots (**a**), we can see number of features on the x-axis, on the y-axis are the different ML algorithms (GB, SVC, LDA, and LR), and the accuracy is on the z-axis. For each ML algorithm, we present the evolution of features selected in IVI as a continuous line and the features selected by the filter as a dashed line. In (**b**), we show a comparatison between filters and IVI. For clarity and simplicity, we only show one comparative sample. In (**b**), the red line represents a dummy classifier that makes predictions with the most frequent class, and the green dashed line represents the results of the classifier trained with all features. number of features to just the relevant ones, in an attempt to visualize this effect across the different plots. In the same way as it previously analyzed in the synthetic dataset, the contributions to the decision process of the weights of the features in all the experiments were evaluated. Again, (i) the smaller contributions or weights of a number of variables meant they were classified as noisy or redundant, (ii) the large contributions of other variables classified them as relevant, and (iii) a number of them had small to medium contributions and were redundant, but were misclassified.