Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. ,  theoretically compare PU (and NU) learning against PN learning based on the upper bounds on estimation errors.  find simple conditions when PU and NU learning are likely to outperform PN learning, and  prove that, in terms of the upper bounds, either PU or NU learning (depending on the class-prior probability and the sizes of P and N data) given infinite U data will improve on PN learning. Our theoretical findings ll agree with the experimental results on artificial and benchmark data even when the experimental setup does not match the theoretical assumptions exactly.
['Gang Niu', 'Marthinus Christoffel du Plessis', 'Tomoya Sakai', 'Yao Ma', 'Masashi Sugiyama']
I am writing a research paper on a idea Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
[{"from": "human", "value": "I am writing a research paper on a idea Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. ,  theoretically compare PU (and NU) learning against PN learning based on the upper bounds on estimation errors.  find simple conditions when PU and NU learning are likely to outperform PN learning, and  prove that, in terms of the upper bounds, either PU or NU learning (depending on the class-prior probability and the sizes of P and N data) given infinite U data will improve on PN learning. Our theoretical findings ll agree with the experimental results on artificial and benchmark data even when the experimental setup does not match the theoretical assumptions exactly."}]