## 4 Experiments and Results
### Latent Space Representation and Classification  
Following the methodology mentioned in Section 3.5, in this experiment we propose to evaluate the classification ability in a latent space in 3D (for representability reasons). To achieve this, we first proceed to perform the projection on the latent space, using an autoencoder with the selected features defined in Section 4.1. In this way, and to obtain a higher dispersion in the latent space, we applied a fine-tuning by adding a softmax layer mentioned in Section 3.6. Then, a classifier (SVC) was implemented in the resulting latent space, which allows us to evaluate the prediction capability in this new space for the different scenarios under study. In other words, the experiment allows us to evaluate  
Figure 4: Results of IVI algorithm for each ML technique in the synthetic dataset (**a**), in the German Credit Dataset (**b**), and in the PaySim (**c**). 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 color represents scenarios where feature was identified as relevant. Red color represents features not identified as relevant during the analysis. The threshold for determining relevance is defined by MIFF filter (at least in two methods).  
how the transformation from the input space to the latent space contributes to the possible improvement in terms of accuracy. In an attempt to verify and quantify the results, accuracy was calculated in three different scenarios for each dataset. The scenarios considered different sets of features, namely, (i) the complete features available in input space; (ii) IVI with MIFF classified features in the input space; and (iii) IVI with MIFF classified features in the latent space. SVC was implemented as the classifier for benchmarking and analysis. Table 4 summarizes the mean and standard deviation of the 100 resampling executions for the different scenarios. The results showed that the latent space consistently provided the best results for all datasets. The relatively small standard deviation of the results obtained after multiple resampling of the input signal encourages us to validate the results obtained.  
In Table 4, columns from left to right correspond to the inclusion of all available features _Acc_all_features_, of IVI with MIFF filter _Acc_fs_MIFF_, and of IVI with MIFF filter in the latent space (using autoencoder) _Acc_fs_MIFF_LS_. As we can see in the results in this table, columns _Acc_all_features_ (SVC) and _Acc_fs_MIFF_ represent the values obtained in the companion paper [1], where it was compared with several alternatives, using all attributes and the MIFF filter for the synthetic and German datasets. In this sense, we can consider _Acc_all_features_ (SVC) as the baseline and the _Acc_fs_MIFF_ as the gold standard. In column _Acc_fs_MIFF_LS_, we obtain the best results in the latent space and it is clear that in the latent space the ML algorithms improve the classification task by better mapping the different types of transactions. Furthermore, in this column we also observed a decrease in the standard deviation of up to almost 10 times in both synthetic and PaySim datasets and 2 times in the German Dataset. This indicates that the use of latent space not only improves the accuracy, but also increases the stability of the results.