Data augmentation solutions for tabular/structured data
It's hard in general, because you don't know the joint distribution of the data, let alone joint distribution with the label -- or else you wouldn't need a classifier. Without that you can't confidently sample new, valid instances.

In the image case it's 'easy' because we know certainly that a rotation or shear or scale of an image produces another valid image, and we know its label is the same.

If you're willing to make the assumption that points in your input space between two instances with the same label are also a) valid inputs and b) have the same label, then you can apply things like SMOTE (https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html) to generate more data according to that assumption. If that assumption is wrong, it'll hurt performance.