## 3 Materials and Methods
### Informative Variable Identifier
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**method\_Classifier** & **Acc\_all\_features** & **IVI (Relevant)** & **Acc\_fs\_MIFF** & **Acc\_fs\_RFF** \\ \hline GB & \(0.9393\pm 0.005034\) & \(0.9319\pm 0.004903\) & \(0.9393\pm 0.005209\) & \(0.8961\pm 0.005413\) \\ SVC & \(0.9870\pm 0.003195\) & \(0.9705\pm 0.003803\) & \(0.9872\pm 0.002951\) & \(0.9062\pm 0.005112\) \\ LDA & \(0.9787\pm 0.003127\) & \(0.9709\pm 0.003638\) & \(0.9877\pm 0.003356\) & \(0.9063\pm 0.004634\) \\ LR & \(0.9865\pm 0.002669\) & \(0.9711\pm 0.003586\) & \(0.9881\pm 0.003579\) & \(0.9062\pm 0.004958\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical results for accuracy of the ML methods in synthetic dataset. Mean and standard deviation of the results for one-hundred-resample analysis. In rows are the results for the different ML methods. In columns are the different set of features included in the analysis. Columns from left to right, represent all available features, IVI-relevant features, MIFF features, and RFF features. Figure 3: 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 as each feature 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 comparison 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 shows the results of the classifier trained with all features. A third experiment aimed to measure the contribution of each of the variables to the final model. Figure 5, collects the weights for the MIFF analysis, and can be understood as the contributions of the different experiments. Additionally, as can be appreciated in the figure, although all features presented similar weights, \(f3\) and \(f1\) received slightly higher weights than the others. An exception to this was found for \(f5\), which had a small weight in all ML algorithms we analyzed. This result is consistent with the fact that this feature was effectively irrelevant and misclassified by the algorithm, thereby allowing further adjustment to the model. Figure 4: Graphical results for accuracy through of incorporation of the features in sequential order of relevance to MIFF models, enabling us see the evolution in accuracy as each feature 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 comparison 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 shows the results of the classifier trained with all features. ### German Credit Dataset

In this experiment, we applied the knowledge previously acquired in the synthetic dataset, but this time using actual data from the German credit dataset. The main purpose of this was to verify if the results obtained with real data would be the same, when using the same methodology as for the synthetic dataset. Following the previously scrutinized methodology, our FS technique was applied to this new dataset.