## 4 Discussion  
SMOTE and its different variant methods, including Borderline-SMOTE, ADASYN, SMOTE-Tomek, etc., only deal with continuous features. However, in the real world, datasets often consist of both continuous and categorical features. One way to use variants of these SMOTE techniques on such datasets is by one-hot encoding categorical features first and then applying the oversampling method to them. However, it generates two major problem: firstly, in cases where the dataset contains several multi-label nominal features, one-hot encoding increases data dimension significantly, which in turn brings the curse of dimensionality [33]. Secondly, being unaware of nominal features, these SMOTE variants produce multiple new labels of these attributes in the synthetically generated resampled data, which do not have any physical significance. SMOTE-NC resolves these problems, but each label is considered to have equal propensity towards the target class, muddling the feature's contribution towards distance calculation, whereas our developed variation, SMOTE-ENC, differentiates the label of association between labels of a particular nominal feature, capturing the nominal feature's contribution in distance calculation more precisely. For example, let us suppose there is a dataset which contains two multi-level categorical features (c1 and c2) along with a few continuous features. In the SMOTE-NC algorithm, the inter-level distance of c1 feature will be the same as the inter-level distance of the c2 feature because in SMOTE-NC, distance between any two levels of the nominal attributes is the median of standard deviations of the continuous features of the data. In other words, inter-level distance of the nominal feature is not dependent on that feature, rather on the continuous attributes of the dataset. In contrast, in our version of the SMOTE algorithm, inter-level distance of the c1 feature will be different than that of the c2 feature and the distance will not be dependent on the continuous features, rather on that particular nominal feature's distribution. Hence, if the inter-level distance of c1 is more than that of c2, c1 will have a higher contribution while calculating distance between two data points. ROC and precision-recall curves in the Section 3 shows, SMOTE-ENC yielded better results in four out of five datasets. In these cases, when resampled data was applied to the random forest classifier, nominal features turned out to be significant drivers. From this observation, it can be inferred that SMOTE-ENC is likely to yield better results when there is some association between the categorical features and the target class. When the association is poor, SMOTE-ENC is not able to yield better results than SMOTE-NC. From the Table 2, it can be observed that, even when ROC and precision-recall curves show SMOTE-ENC and SMOTE-NC to yield similar performance, there was a difference between these methods' performance in terms of precision and recall. For banking, credit card and rain datasets, SMOTE-ENC generated better precision, for forest cover dataset, it produced improved recall. Thus, SMOTE-ENC and SMOTE-NC can be differentiated when either precision or recall had higher priority and \(F_{\beta}\) score was a better evaluator than F1-score. From Figures 1 and 3, it is also evident that SMOTE-ENC outperformed SMOTE-NC method substantially on banking and on credit card datasets. For the rest of the datasets, the new algorithm could not attain significant improvement. It was observed that in these two datasets (Table 2), the proportion of the number of nominal features to total number of features was more than 25%, whereas for rest of the datasets this proportion was less than 20%. From this observation, it can be inferred that, in these two datasets, a change in interpretation of the nominal features could bring a considerable amount of change in interpretation of the whole dataset. This might be the reason why SMOTE-ENC was able to achieve substantial improvement on these datasets.