[
Argyriou, A., Evgeniou, T. and Pontil, M. (2008). Convex multi-task feature learning, Machine Learning 73(3): 243–272.10.1007/s10994-007-5040-8
]Search in Google Scholar
[
Bekker, J. and Davis, J. (2018). Estimating the class prior in positive and unlabeled data through decision tree induction, Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, pp. 1–8.
]Search in Google Scholar
[
Bekker, J. and Davis, J. (2020). Learning from positive and unlabeled data: A survey, Machine Learning 109(4): 719–760.10.1007/s10994-020-05877-5
]Search in Google Scholar
[
Bekker, J., Robberechts, P. and Davis, J. (2019). Beyond the selected completely at random assumption for learning from positive and unlabeled data, in U. Brefeld et al. (Eds), Proceedings of the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer, Cham, pp. 71–85.
]Search in Google Scholar
[
Biecek, P. (2018). DALEX: Explainers for complex predictive models in R, Journal of Machine Learning Research 19(1): 3245–3249.
]Search in Google Scholar
[
Bucak, S.S., Jin, R. and Jain, A.K. (2011). Multi-label learning with incomplete class assignments, Proceedings of the Conference on Computer Vision and Pattern Recognition, Colorado Springs, USA, pp. 2801–2808.
]Search in Google Scholar
[
Couso, I., Dubois, D. and Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data, Proceedings of the International Conference on Scalable Uncertainty Management, Granada, Spain, pp. 3–16.
]Search in Google Scholar
[
Dembczyński, K., Waegeman, W., Cheng, W. and Hüllermeier, E. (2012). On label dependence and loss minimization in multi-label classification, Machine Learning 88(1): 5–45.10.1007/s10994-012-5285-8
]Search in Google Scholar
[
Elkan, C. and Noto, K. (2008). Learning classifiers from only positive and unlabeled data, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’08, Las Vegas, USA, pp. 213–220.
]Search in Google Scholar
[
Frenay, B. and Verleysen, M. (2014). Classification in the presence of label noise: A survey, IEEE Transactions on Neural Networks and Learning Systems 25(5): 845–869.10.1109/TNNLS.2013.229289424808033
]Search in Google Scholar
[
Gibaja, E. and Ventura, S. (2015). A tutorial on multilabel learning, ACM Computing Surveys 47(3): 1–38.10.1145/2716262
]Search in Google Scholar
[
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3(1): 1157–1182.
]Search in Google Scholar
[
Hall, E.J. and Brenner, D.J. (2008). Cancer risks from diagnostic radiology, British Journal of Radiology 81(965): 362–378.10.1259/bjr/0194845418440940
]Search in Google Scholar
[
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, New York.10.1007/978-0-387-84858-7
]Search in Google Scholar
[
Hazell, L. and Shakir, S. (2006). Under-reporting of adverse drug reactions: A systematic review, Drug Safety 29(5): 385–396.10.2165/00002018-200629050-0000316689555
]Search in Google Scholar
[
He, Z.-F., Yang, M., Gao, Y., Liu, H.-D. and Yin, Y. (2019). Joint multi-label classification and label correlations with missing labels and feature selection, Knowledge-Based Systems 163(1): 145–158.10.1016/j.knosys.2018.08.018
]Search in Google Scholar
[
Heitjan, D.F. and Rubin, D.B. (1991). Ignorability and coarse data, Annals of Statistics 19(4): 2244–2253.10.1214/aos/1176348396
]Search in Google Scholar
[
Jain, S., White, M. and Radivojac, P. (2016). Estimating the class prior and posterior from noisy positives and unlabeled data, Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, pp. 2693–2701.
]Search in Google Scholar
[
Jaskie, K., Elkan, C. and Spanias, A. (2020). A modified logistic regression for positive and unlabeld learning, 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, pp. 2007–2011.
]Search in Google Scholar
[
Ji, S., Tang, L., Yu, S. and Ye, J. (2010). A shared-subspace learning framework for multi-label classification, ACM Transactions on Knowledge Discovery from Data 4(2): 1–29.10.1145/1754428.1754431
]Search in Google Scholar
[
Kakade, S.M., Shalev-Shwartz, S. and Tewari, A. (2012). Regularization techniques for learning with matrices, Journal of Machine Learning Research 13(1): 1865–1890.
]Search in Google Scholar
[
Kanehira, A. and Harada, T. (2016). Multi-label ranking from positive and unlabeled data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 5138–5146.
]Search in Google Scholar
[
Kashef, S., Nezamabadi-pour, H. and Nikpour, B. (2018). Multilabel feature selection: A comprehensive review and guiding experiments, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(2): 1–29.10.1002/widm.1240
]Search in Google Scholar
[
Lagasse, R.S. (2002). Anesthesia safety: Model or myth?: A review of the published literature and analysis of current original data, Anesthesiology: The Journal of the American Society of Anesthesiologists 97(6): 1609–1617.10.1097/00000542-200212000-0003812459692
]Search in Google Scholar
[
Łazęcka, M., Mielniczuk, J. and Teisseyre, P. (2021). Estimating the class prior for positive and unlabelled data via logistic regression, Advances in Data Analysis and Classification 15(4): 1039–1068.10.1007/s11634-021-00444-9
]Search in Google Scholar
[
Lee, J. and Kim, D.-W. (2017). SCLS: Multi-label feature selection based on scalable criterion for large label set, Pattern Recognition 66(1): 342–352.10.1016/j.patcog.2017.01.014
]Search in Google Scholar
[
Natarajan, N., Dhillon, I.S., Ravikumar, P. and Tewari, A. (2013). Learning with noisy labels, Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS’13, Lake Tahoe, USA, pp. 1196–1204.
]Search in Google Scholar
[
Naula, P., Airola, A., Salakoski, T. and Pahikkala, T. (2014). Multi-label learning under feature extraction budgets, Pattern Recognition Letters 40(1): 56–65.10.1016/j.patrec.2013.12.009
]Search in Google Scholar
[
Pereira, R.B., Plastino, A., Zadrozny, B. and Merschmann, L. H. C. (2018). Categorizing feature selection methods for multi-label classification, Artificial Intelligence Review 49(1): 1–22.10.1007/s10462-016-9516-4
]Search in Google Scholar
[
Plessis, M.C., Niu, G. and Sugiyama, M. (2017). Class-prior estimation for learning from positive and unlabeled data, Machine Learning 106(4): 463–492.10.1007/s10994-016-5604-6
]Search in Google Scholar
[
Ramaswamy, H., Scott, C. and Tewari, A. (2016). Mixture proportion estimation via kernel embeddings of distributions, Proceedings of the 33rd International Conference on Machine Learning, New York, USA, pp. 2052–2060.
]Search in Google Scholar
[
Sechidis, K. and Brown, G. (2018). Simple strategies for semi-supervised feature selection, Machine Learning 107(2): 357–395.10.1007/s10994-017-5648-2695404031983804
]Search in Google Scholar
[
Sechidis, K., Calvo, B., and Brown, G. (2014). Statistical hypothesis testing in positive unlabelled data, Machine Learning and Knowledge Discovery in Databases, Nancy, France, pp. 66–81.
]Search in Google Scholar
[
Sechidis, K., Sperrin, M., Petherick, E.S., Lujan, M. and Brown, G. (2017). Dealing with under-reported variables: An information theoretic solution, International Journal of Approximate Reasoning 85(1): 159–177.10.1016/j.ijar.2017.04.002
]Search in Google Scholar
[
Shalev-Shwartz, S. and Ben-David, S. (2013). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, Cambridge.10.1017/CBO9781107298019
]Search in Google Scholar
[
Sun, Y.-Y., Zhang, Y. and Zhou, Z.-H. (2010). Multi-label learning with weak label, Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI’10, Atlanta, USA, pp. 593–598.
]Search in Google Scholar
[
Teisseyre, P. (2020). Learning classifier chains using matrix regularization: application to multimorbidity prediction, Proceedings of the European Conference on Artificial Intelligence, ECAI 2020, Santiago de Compostela, Spain, pp. 1–8.
]Search in Google Scholar
[
Teisseyre, P. (2021). Classifier chains for positive unlabelled multi-label learning, Knowledge-Based Systems 213(1): 1–16.10.1016/j.knosys.2020.106709
]Search in Google Scholar
[
Teisseyre, P., Mielniczuk, J. and Łazecka, M. (2020). Different strategies of fitting logistic regression for positive and unlabelled data, Proceedings of the International Conference on Computational Science, ICCS 2020, Amsterdam, The Netherlands, pp. 3–17.
]Search in Google Scholar
[
Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J. and Vlahavas, I. (2011). Mulan: A Java library for multi-label learning, Journal of Machine Learning Research 12(1): 2411–2414.
]Search in Google Scholar
[
Walley, N.M. et al. (2018). Characteristics of undiagnosed diseases network applicants: Implications for referring providers, BMC Health Services Research 18(1): 1–8.10.1186/s12913-018-3458-2610692330134969
]Search in Google Scholar
[
Wei, T., Guo, L.-Z., Li, Y.-F. and Gao, W. (2018). Learning safe multi-label prediction for weakly labeled data, Machine Learning 107(4): 703–725.10.1007/s10994-017-5675-z
]Search in Google Scholar
[
Wu, L., Jin, R. and Jain, A.K. (2013). Tag completion for image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3): 716–727.10.1109/TPAMI.2012.12422641703
]Search in Google Scholar
[
Zhang, M. and Zhou, Z. (2013). A review on multi-label learning algorithms, IEEE Transactions on Knowledge and Data Engineering 26(8): 1819–1837.10.1109/TKDE.2013.39
]Search in Google Scholar
[
Zhu, P., Xu, Q., Hu, Q., Zhang, C. and Zhao, H. (2018). Multi-label feature selection with missing labels, Pattern Recognition 74(1): 488–502.10.1016/j.patcog.2017.09.036
]Search in Google Scholar
[
Zufferey, D., Hofer, T., Hennebert, J., Schumacher, M., Ingold, R. and Bromuri, S. (2015). Performance comparison of multi-label learning algorithms on clinical data for chronic diseases, Computers in Biology and Medicine 65(1): 34–43.10.1016/j.compbiomed.2015.07.01726275389
]Search in Google Scholar