## 4 Experiments and Results
### Clustering Through ITR  
Once the STE weights are obtained, we have the contribution of each feature in the model. With this, it is possible to coherently develop a ranking of features according to their contribution, based on the magnitude of the coefficients following the strategy described in Section 3.7.3. We can establish, for each transaction under study, the sequence of features according to their relevance that best approximates the predicted model and its score in the classification strategy carried out. This sequence, and its modeling to obtain it, was described in detail in the methodology, Section 3.5, and it is referred as ITR. This ranking of features or ITR can be considered the profile of the transaction by collecting the sequence of contribution of the features for that transaction.  
Figure 8 shows two examples (in rows) of a set of samples that share the same ITR value for the synthetic dataset. Column (a) shows the corresponding ITR in such a way that in the first row, we can see that for this set of transactions the ITR shows that the informative features in the decision process are ordered as \(f4,f5,f2,f0,f1,f3\), while for the set in the next row, they are ordered as \(f4,f5,f3,f2,f1,f0\). In these ITR we can observe that attribute \(f3\) for the second set has a high relevance, while for the first set it is the last one. In column (b), we represent the latent space which has been generated with the the encoder defined in Section 3.6. In said latent space, the transactions are marked according to whether or not they were correctly classified by the generated model. Thus, it can be seen that the elements in blue correspond to fraudulent cases correctly identified and the elements in red correspond with non-fraudulent cases correctly identified. Additionally, the transactions of both classes that were incorrectly classified are represented in green and yellow. This is visible in the set of transactions that share the ITR of the first row, since, in the case of the second row, 100% of the transactions correspond to the same class and have been correctly classified, as they are sufficiently unclassified from the visual border. It can be seen how the misclassified transactions, which are also collected for the reader's convenience in column (c), are in the visual border zone of the two classes in the latent space, being consistent with the classification strategy in this space. Finally, note that column (d) incorporates the confusion matrix.  
Figure 7: Latent space for the different datasets. (**a**) Synthetic dataset. (**b**) PaySim Dataset. Red samples correspond to non-fraudulent transactions. Blue samples correspond to fraudulent instances. Fraud and non-fraud are easily observed in separate areas, although both groups are not always clearly separable.  
In Figure 9, similar representations of the same figures and contents are reproduced as in the previous Figure 8, but in this case, for three sets of transactions that share the same ITR for the PaySim Dataset. Results show the same behavioral patterns as in the synthetic dataset, where a strong relationship is observed between the transactions that share the same ITR value, sometimes effectively corresponding to transactions of the same class, although not in all cases, since the transactions located in the interface areas of classes generate a limited number of cases corresponding to the other class.  
Figure 8: ITR analysis. By rows, the analysis of 2 sets of samples sharing same ITR for the synthetic dataset. By column: (**a**) represents the ITR, (**b**) latent space representation of the samples identifying the class and the AE prediction, (**c**) latent space of misclassified samples, (**d**) the confusion matrix for all the transactions sharing the same ITR.