I have a merchants dataset with 800,000 samples and 18,000 labels. Each sample is associated with a single label and the labels are independent.
An example sample looks like
description, label int'l 0028240525 amazon uk retail amazon.co.uk, Amazon
In addition to the existing samples there will be new retailers added to the dataset. In this case there may well only be a single sample for that new retailer.
To summarise, I need a classifier that
- handles a large number of labels (~18,000, independent, single label per sample)
- is able to classify undersampled labels (i.e. a single retailer)
Is there an approach that will handle both? Perhaps two separate classifiers makes more sense?