Exploring Positive Unlabeled Machine Learning Public
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Positive and unlabeled learning involves positive examples and unlabeled data. The unlabeled data can contain both positive and negative examples. PU learning has gained prevalence recently due to its newfound application in social media and medicine. The current state of the art approaches to PU algorithms face a multitude of issues. Therefore the team implemented and conducted experiments on existing algorithms such as SAR-EM and NNPU. These algorithms were modified to create a novel PU algorithm.
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