## 3 Materials and Methods
### Informative Variable Identifier
Among these 20 features, 17 of them are categorical and three are continuous. All these features are commonly used in CFDs, and some examples are: credit purpose, savings, present employment, and credit amount. There are no missing values. To facilitate FS and in order to train the models, the values of the three continuous attributes were normalized, and for the discrete features, they were converted to one-hot encoding. After these pre-processing stages, the final dataset was 61-dimensional. ### Analyzing the Synthetic Dataset

As we introduced earlier, we first applied the novel FS strategy based on the IVI algorithm to identify the relevant features. This effort was intensively executed by the five different algorithms introduced earlier, namely, CME, SVC, GB, LR, and LDA. This approach allowed us to achieve a more unbiased perspective of the real effective potential of selected features, and of the sustainability and consistency across methods. Figure 2 summarizes the outcome of the IVI algorithm for each individual ML technique. In this figure, validated selected features (columns) are in green and those ones not identified as significant by the algorithm (rows) are in red. We should recall at this point that features identified as relevant for all ML methods were classified as categorized as RFF, as they recurrently and consistently were relevant in all methods. Features \(f1\)-\(f4\) were all included in this set, but f0 was not identified as such due to the miss-classification by SVC. In the same vein, features identified as relevant for at least two methods were understood to be informative for further analysis and so were chosen for the MIFF group of variables. For this specific case, features \(f0\)-\(f5\) met the MIFF criteria and were included as members of this filter. These features perfectly match with the relevant features on the synthetic dataset (\(f0\)-\(f4\)), other than one redundant feature (\(f5\)). Attending to these results, we can conclude that the IVI algorithm was consistent with the different ML methods, so it appears to have a valid feature selection ability. In an attempt to verify and quantify the results, accuracy was calculated in sixteen different scenarios, for SVC, GB, LR, and LDA, and considering different sets of features, namely: (i) all the available features; (ii) only the relevant features according to IVI for each corresponding ML method; (iii) MIFF-classified features; (iv) RFF-classified features. CME was not considered for this task, as CME is a fast weight-generator method but is not a classifier. Table 1 summarizes the means and standard deviations of the 100 resampling executions for 16 different scenarios. This table shows how the accuracy remained mostly invariant among all the classification methods, as the different columns reflect almost no change in terms of mean or standard deviation. The only exception can be found in the case of GB, which in all cases, still shows a smaller classification capability to the rest of the analyzed methods. Similarly, in the case of LDA, when applied to all the available variables, a slight reduction in its prediction capacity can be appreciated. The benchmark illustrates that the best results were obtained when the MIFF filter was used, and there was equivalent predictive power when all the variables were used, reaching in both cases the predictive accuracy of 98.8%. The standard deviation was in all cases lower than 4%. The IVI model offered in all cases, very similar results to the outstanding methods (97% accuracy). On the contrary, the RFF filtering suffered in its predictive capacity compared to the rest of the models, showing the lack of expressive power (90% accuracy) due to the non-incorporation of variables as a consequence of the incorrect classification of relevant variables. Figure 2: Results of the IVI algorithm for each ML technique on the synthetic dataset.