Table shows the accuracy of each classifier with respect to the oversampled method applied to the database that they were optimized and trained. Although all classifiers presented an accuracy score of 0.80 or higher, we selected those with the highest accuracy, labeled SMOTE-ET, SMOTE-RF, sSMOTE-KNN, sSMOTE-ET, and sSMOTE-RF, to be tested using structures that are not present in the database. The label of the models is: SMOTE and sSMOTE refer to the oversampling method used (SMOTE and SVMSMOTE, respectively) and the two or three letters after the hyphen refers to the classifier (ET for extra tree, RF for random forest, and KNN for knearest neighbors). Figure shows the confusion matrix for these models and one can see that the highest numbers are in the diagonal for all structural groups, meaning that all models correctly predicted most of the structural groups in the test partition of the database with a few mistakes.