I've been looking for papers that discuss the pros and cons of positive unlabeled learning but I haven't been able to find anything.
I'm looking to compare the general differences between creating a positive-unlabeled based problem vs a regression classification. I have a biological dataset where it's hard to definitively define a sample as negative but I can make rules that would find something as close as possible to negative - with my idea being that I can assign scores to samples (e.g. like 0.1 instead of 0 to imply the lack of certainty but the closeness of the sample to being negative). However, I am trying to get an understanding of if I should consider positive unlabelled learning (in theory I could label my positive samples and ignore everything else even if other samples are capable of having a close to negative label/score) but I'm struggling to find information on pros and cons of trying positive-unlabelled learning.