References
Research papers:
1. "Learning classifiers from only positive and unlabeled data" by Elkan, Charles, and Keith Noto (http://www.eecs.tufts.edu/~noto/pub/kdd08/elkan.kdd08.pdf)
2. “Analysis of Learning from Positive and Unlabeled Data” by Plessis, Sugiyama, and Niu (https://papers.nips.cc/paper/5509-analysis-of-learning-from-positive-and-unlabeled-data.pdf)
3. “Efficient Training for Positive Unlabeled Learning” by Sansone, Natale, and Zhou (https://arxiv.org/pdf/1608.06807.pdf)
Open Source Code:
1. Positive and Unlabeled Learning(pu-learning) (https://github.com/aldro61/pu-learning)
Data Sets:
1. Breast Cancer Wisconsin (Original) Data Set (http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28original%29)
1. "Learning classifiers from only positive and unlabeled data" by Elkan, Charles, and Keith Noto (http://www.eecs.tufts.edu/~noto/pub/kdd08/elkan.kdd08.pdf)
2. “Analysis of Learning from Positive and Unlabeled Data” by Plessis, Sugiyama, and Niu (https://papers.nips.cc/paper/5509-analysis-of-learning-from-positive-and-unlabeled-data.pdf)
3. “Efficient Training for Positive Unlabeled Learning” by Sansone, Natale, and Zhou (https://arxiv.org/pdf/1608.06807.pdf)
Open Source Code:
1. Positive and Unlabeled Learning(pu-learning) (https://github.com/aldro61/pu-learning)
Data Sets:
1. Breast Cancer Wisconsin (Original) Data Set (http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28original%29)
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