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?