Particle Competition for Pseudo-Labeling in Positive and Unlabeled Learning

• Hemilyn S. A. Porfirio UFSCar
• Guilherme Henrique Messias UFSCar
• Alan Demétrius Baria Valejo UFSCar

DOI: https://doi.org/10.5753/eniac.2026.31553

Resumo

Supervised algorithms require labeled examples from all classes, but obtaining negative labels can be costly or infeasible. This setting characterizes Positive and Unlabeled Learning (PUL), in which only positive examples and unlabeled data are available. This paper introduces PC-PUL, which adapts Particle Competition to select reliable negatives by exploiting data topology. Autonomous particles compete for node ownership and form territories, from which nodes in negative territories that are topologically distant from positive ones are selected. Experiments on five datasets show superior results on CiteSeer and competitive performance on PubMed and MNIST, indicating that PC-PUL is a promising alternative for reliable-negative selection in PUL.

Referências

Bekker, J. and Davis, J. (2020). Learning from positive and unlabeled data: a survey. Machine Learning, 109(5):703–750.

Breve, F., Zhao, L., Quiles, M., Pedrycz, W., and Liu, J. (2012). Particle competition and cooperation in networks for semi-supervised learning. Knowledge and Data Engineering, IEEE Transactions on, 24:1686–1698.

Breve, F. A., Zhao, L., and Quiles, M. G. (2010). Semi-supervised learning from imperfect data through particle cooperation and competition. In The 2010 International Joint Conference on Neural Networks (IJCNN), pages 1–8.

Carnevali, J. C., Rossi, R. G., Milios, E., and Lopes, A. d. A. (2021). A graph-based approach for positive and unlabeled learning. Information Sciences, 580:655–672.

Chaudhari, S. and Shevade, S. (2012). Learning from positive and unlabelled examples using maximum margin clustering. In Huang, T. et al., editors, Neural Information Processing, pages 465–473, Berlin, Heidelberg. Springer Berlin Heidelberg.

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische mathematik, 1(1):269–271.

Hagberg, A. A., Schult, D. A., and Swart, P. J. (2008). Exploring network structure, dynamics, and function using networkx. In Varoquaux, G., Vaught, T., and Millman, J., editors, Proceedings of the 7th Python in Science Conference, pages 11–15, Pasadena, CA USA.

Jaskie, K. and Spanias, A. (2019). Positive and unlabeled learning algorithms and applications: A survey. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–8.

LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.

Li, X. and Liu, B. (2003). Learning to classify texts using positive and unlabeled data. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03), pages 587–592.

Liu, L. and Peng, T. (2014). Clustering-based method for positive and unlabeled text categorization enhanced by improved TFIDF. Journal of Information Science and Engineering, 30(5):1463–1481.

Ma, S. and Zhang, R. (2017). PU-LP: A novel approach for positive and unlabeled learning by label propagation. In 2017 IEEE International Conference on Multimedia Expo Workshops (ICMEW), pages 537–542.

Messias, G. H., Iasulaitis, S., and Valejo, A. D. B. (2025). Graph neural networks for positive and unlabeled learning: a rewiring approach. Knowledge and Information Systems.

Namata, G., London, B., Getoor, L., Huang, B., and Edu, U. (2012). Query-driven active surveying for collective classification. In Proceedings of the 10th International Workshop on Mining and Learning with Graphs.

Quiles, M. G., Zhao, L., Alonso, R. L., and Romero, R. A. F. (2008). Particle competition for complex network community detection. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18(3):033107.

Rozemberczki, B., Allen, C., and Sarkar, R. (2021). Multi-scale attributed node embedding. In Journal of Complex Networks, volume 9, page cnab014.

Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T. (2008). Collective classification in network data. AI Magazine, 29(3):93–106.

Souza, M. C. d., Rossi, R. G., Rezende, S. O., and Carvalho, A. C. P. L. F. d. (2021). A network-based positive and unlabeled learning approach for fake news detection. Machine Learning, 111(10):3549–3592.

Wu, M., Pan, S., Du, L., and Zhu, X. (2021). Learning graph neural networks with positive and unlabeled nodes. ACM Transactions on Knowledge Discovery from Data, 15(6):1–25.

Yang, P., Yang, Y. H., Zhou, B. B., and Zomaya, A. Y. (2012). Positive-unlabeled learning for disease gene identification. Bioinformatics, 28(20):2640–2647.

Zhu, X. and Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers.