I have a dataset with 77 different labels. Each sample has one or more of these labels.
I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of examples that have a particular label, whereas the other labels don't occur so frequently across the data samples.
I'm trying to use SMOTE to synthesize new data samples for the minority labels but apparently, imblearn's SMOTE doesn't support multi-label data. Is there an alternative to SMOTE that I can use for multilabel classification, or should I treat my problem as 77 different binary classification problems, and apply SMOTE on each iteration separately?