1. Basile, T. M., Di Mauro, N., Esposito, F., Ferilli, S., & Vergari, A. (2018). Density estimators for positive-unlabeled learning. In New frontiers in mining complex patterns: 6th international workshop, NFMCP 2017, held in conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Revised Selected Papers (Vol. 10785, pp. 49–64). Berlin: Springer.
2. Bekker, J., & Davis, J. (2018a). Estimating the class prior in positive and unlabeled data through decision tree induction. In Proceedings of the 32th AAAI conference on artificial intelligence (pp. 2712–2719).
3. Bekker, J., & Davis, J. (2018b). Positive and unlabeled relational classification through label frequency estimation. In N. Lachiche & C. Vrain (Eds.), Inductive logic programming (pp. 16–30). Cham: Springer.
4. Bekker, J., Robberechts, P., & Davis, J. (2019). Beyond the selected completely at random assumption for learning from positive and unlabeled data. In ECML PKDD: Joint European conference on machine learning and knowledge discovery in databases. Cham: Springer.
5. Blanchard, G., Lee, G., & Scott, C. (2010). Semi-supervised novelty detection. Journal of Machine Learning Research, 11, 2973–3009.