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Positive Unlabeled (PU) learning is widely used in many applications, where abinary classifier is trained on the datasets consisting of only positive andunlabeled samples. In this paper, we improve PU learning over state-of-the-artfrom two aspects. Firstly, existing model evaluation methods for PU learningrequires ground truth of unlabeled samples, which is unlikely to be obtained inpractice. In order to release this restriction, we propose an asymptoticunbiased practical AUL (area under the lift) estimation method, which makes useof raw PU data without prior knowledge of unlabeled samples.  Secondly, we propose ProbTagging, a new training method for extremelyimbalanced data sets, where the number of unlabeled samples is hundreds orthousands of times that of positive samples. ProbTagging introduces probabilityinto the aggregation method. Specifically, each unlabeled sample is taggedpositive or negative with the probability calculated based on the similarity toits positive neighbors. Based on this, multiple data sets are generated totrain different models, which are then combined into an ensemble model.Compared to state-of-the-art work, the experimental results show thatProbTagging can increase the AUC by up to 10%, based on three industrial andtwo artificial PU data sets.

Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets