Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning
Table 2: Mean Minor Accuracy
['#Minor', 'Baseline', 'Under', 'Over', 'SMOTE', 'Perturbed', 'Cavity Filling']
We also verified the mean accuracy, precision, recall, and F1 of the minor classes alone ,and they are the macro averages over minor classes because the scores in minor classes are important at times. They are illustrated in Fig. Undersampling, SMOTE, perturbation, and cavity filling exhibit identical mean precision, recall, and F1 scores of minor classes and cannot be distinguished in the figures. The scores are summarized in Tab. Although undersampling exhibits improved mean minor recall, cavity filling is better than or as good as undersampling and oversampling with respect to the mean minor accuracy, precision, and F1. Further, cavity filling and undersampling can be combined. We can use undersampling and cavity filling for other cases when the recall of minor classes is important.