I am dealing with intent classification task on an Italian customer service data set.
I've more or less 1.5k sentences and 29 classes (imbalanced).
According to the literature, a good choice is to generate synthetic data, oversampling, or undersampling the training data, using for example the SMOTE algorithm.
I also want use a cross-validation mechanism (stratified k-fold) to be more confident in the obtained result.
I also know that accuracy is not the right metric to take into account, I should use precision, recall and confusion matrix.
Is it possible to combine cross validation k-fold and oversampling or undersampling techniques?