Looking for binary class datasets with high class imbalance, that also have intra-class imbalance in the minority class Newbie question alert...
For a college project I want to compare a few variants of SMOTE in terms of how much they improve classification of the minority class, over using random oversampling.
I have a specific interest in the idea that the minority class may contain small disjuncts that may themselves exhibit imbalance within the class.
I am already looking at the credit card fraud dataset on Kaggle (https://www.kaggle.com/mlg-ulb/creditcardfraud)
Can anyone please point me towards other datasets that have the following kinds of properties:

a reasonably large number of examples (ideally at least a few thousand)
have only two class labels
are highly imbalanced, i.e. the minority class is severely under-represented
ideally the minority examples would have some intra-class imbalance too

Or even better, is there any kind of good search tool out there for finding datasets based on these kinds of characteristics?
The imblearn.datasets package (documentation is here) has a function called fetch_datasets() which is described as:

fetch_datasets allows to fetch 27 datasets which are imbalanced and binarized

I do not know them in sufficient depth to know whether they meet all four of the criteria you've listed, but these may be a good first place to start with.