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Aminian, E., R. P. Ribeiro, and J. Gama (2025). “Histogram approaches for imbalanced data streams regression.” In: Machine Learning, vol. 114, no. 12, p. 274. Springer, 2025, DOI: 10.1007/S10994-025-06892-0.
Nogueira, B., G. M. Menezes, R. P. Ribeiro and N. Moniz (2025). “Experiential-informed data reconstruction for fishery sustainability and policies in the Azores”. In: Discover Data, vol. 3, no. 1, p. 16. Springer. DOI: 10.1007/s44248-025-00034-6.
Shaji, N., S. Tabassum, R. P. Ribeiro, J. Gama, J. Gorgulho, A. Garcia and P. Santana (2025). “Network-based Anomaly Detection in Waste Transportation Data with Limited Supervision”. In: Applied Network Science. DOI: 10.1007/s41109-025-00753-4.
Gama, J. and Ribeiro, R. P. and Mastelini, S. and Davari, N. and Veloso, B. “From Fault Detection to Anomaly Explanation: A Case Study on Predictive Maintenance”. In Journal of Web Semantics, p. 100821. Elsevier, 2024, DOI: 10.1016/j.websem.2024.100821.
Vaz, M. and Summavielle, T. and Sebastiao, R. and Ribeiro, R. P. “Multimodal Classification of Anxiety Based on Physiological Signals.”. In Applied Sciences-Basel, vol. 13, no. 11, p. 6368. MDPI, 2023, DOI: 10.3390/app13116368.
Tome, E. and Ribeiro, R. P. and Dutra, I. and Rodrigues, A. “An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems.” In Sensors, vol. 23, no. 10, p. 4902. MDPI, 2023, DOI: 10.3390/s23104902.
Veloso, B and Gama, J. and Ribeiro, R. P. and Pereira, P. “The MetroPT dataset for predictive maintenance.” In Scientific Data, vol. 9, no. 1. Nature Portfolio, 2022, DOI: 10.1038/s41597-022-01877-3.
Gama, J. and Ribeiro, R. P. and Veloso, B. “Data-Driven Predictive Maintenance.” In IEEE Intelligent Systems, vol. 37, no. 4, pp. 27–29. IEEE, 2022, DOI: 10.1109/mis.2022.3167561.
Aminian, E. and Ribeiro, R. P. and Gama, J. “Chebyshev approaches for imbalanced data streams regression models.” In Data Mining and Knowledge Discovery, vol.~{35}, no.~{6}, pp. {2389–2466}. Springer, 2021, DOI: 10.1007/s10618-021-00793-1.
Ribeiro, R. P. and N. Moniz (2020). “Imbalanced regression and extreme value prediction”. In: Machine Learning. DOI: 10.1007/s10994-020-05900-9.
Portela, E, R. P. Ribeiro and J. Gama (2019). “The search of conditional outliers”. In: Intell. Data Anal. 23.1, pp. 23-39. DOI: 10.3233/IDA-173619.
Branco, P, L. Torgo and R. P. Ribeiro (2019). “Pre-processing approaches for imbalanced distributions in regression”. In: Neurocomputing 343, pp. 76-99. DOI: 10.1016/j.neucom.2018.11.100.
Branco, P, L. Torgo and R. P. Ribeiro (2018). “Resampling with neighbourhood bias on imbalanced domains”. In: Expert Systems 35.4. DOI: 10.1111/exsy.12311.
Branco, P, L. Torgo and R. Ribeiro (2016). “A Survey of Predictive Modeling on Imbalanced Domains”. In: ACM Comput. Surv. 49.2, pp. 31:1-31:50. DOI: 10.1145/2907070.
Ribeiro, R. P, P. M. Pereira and J. Gama (2016). “Sequential anomalies: a study in the Railway Industry”. In: Machine Learning 105.1, pp. 127-153. DOI: 10.1007/s10994-016-5584-6.
Torgo, L, P. Branco, R. P. Ribeiro and B. Pfahringer (2015). “Resampling Strategies for Regression”. In: Expert Systems 32.3, pp. 465-476. DOI: 10.1111/exsy.12081.
Ribeiro, R. and L. Torgo (2008). “A Comparative Study on Predicting Algae Blooms in Douro River, Portugal”. In: Ecological Modelling 212.1-2, pp. 86-91. DOI: 10.1016/j.ecolmodel.2007.10.018.
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Galiza, B., M. Seoane-Santos and R. P. Ribeiro (2026). “A Green AI Perspective on Data-Centric Approaches for Imbalanced Classification”. In: International Workshops of ECML PKDD 2026: [DEARING] 3rd International Workshop on Data-Centric Artificial Intelligence. Communications in Computer and Information Science. Springer. (to appear).
Moutinho, H., R. P. Ribeiro and J. Gama (2026). “Growing Smarter, Not Larger: Instance Selection for Streaming Regression Rule Learners”. In: International Workshops of ECML PKDD 2026: [SCLW] Streaming Continual Learning Workshop. Communications in Computer and Information Science. Springer. (to appear).
Barbosa, I. A., B. Veloso, R. P. Ribeiro and J. Gama (2026). “An Empirical Study on the Robustness of Local Explanation Methods”. In: Explainable Artificial Intelligence - 4th World Conference, XAI 2026. Communications in Computer and Information Science. Springer. (to appear).
Paim, A., J. Gama, B. Veloso, F. Enembreck and R. Ribeiro (2025). “Efficient Instance Selection in Tree-Based Models for Data Streams Classification”. In: 40th ACM/SIGAPP Symposium on Applied Computing (SAC 2025), pp. 448–455. ACM. DOI: 10.1145/3672608.3707782.
Mozolewski, M., S. Bobek, R. Ribeiro, G. Nalepa and J. Gama (2024). “Towards Evaluation of Explainable Artificial Intelligence in Streaming Data”. In: 2nd World Conference on Explainable Artificial Intelligence (XAI 2024), pp. 145–168. Springer Nature Switzerland. DOI: 10.1007/978-3-031-63803-9_8.
Jesus, S., P. Saleiro, I. O. e Silva, B. M. Jorge, R. P. Ribeiro, J. Gama, P. Bizarro and R. Ghani (2024). “Aequitas Flow: Streamlining Fair ML Experimentation”, vol. 25, pp. 1–7.
Ribeiro, R., S. Mastelini, N. Davari, E. Aminian, B. Veloso and J. Gama (2023). “Online Anomaly Explanation: A Case Study on Predictive Maintenance”. In: Joint Workshops Proceedings of ECML PKDD 2022. Communications in Computer and Information Science, vol. 1753, pp. 383–399. Springer. DOI: 10.1007/978-3-031-23633-4_25.
Silva, A., R. Ribeiro and N. Moniz (2022). “Model Optimization in Imbalanced Regression”. In: 25th International Conference on Discovery Science (DS 2022). Lecture Notes in Artificial Intelligence, vol. 13601, pp. 3–21. Springer. DOI: 10.1007/978-3-031-18840-4_1.
Jesus, S., J. Pombal, D. Alves, A. Cruz, P. Saleiro, R. Ribeiro, J. Gama and P. Bizarro (2022). “Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation”. In: 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), Datasets and Benchmarks Track. Advances in Neural Information Processing Systems.
Pinheiro, A. P. and R. P. Ribeiro (2026). “CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression”. In: Advances in Intelligent Data Analysis XXIV (IDA 2026), pp. 312–326. Springer.
Alves, B., A. Almeida, C. Silva, D. Pais, R. P. Ribeiro, J. Gama, J. M. Fernandes, S. Brás and R. Sebastião (2025). “Emotion-Enhanced Pain Assessment Protocol”. DOI: 10.1007/978-3-031-84595-6_23.
Andrade, C., R. P. Ribeiro and J. Gama (2025). “Evaluating Short Text Stream Clustering on Large E-commerce Datasets”. DOI: 10.1007/978-3-031-79035-5_17.
Shaji, N., S. Tabassum, R. P. Ribeiro, J. Gama, P. Santana and A. Garcia (2025). “Network-Based Anomaly Detection in Waste Transportation Data”. DOI: 10.1007/978-3-031-82427-2_23.
Aminian, E., R. P. Ribeiro, and J. Gama (2019). “A Study on Imbalanced Data Streams”. In: Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings, Part II. Vol. 1168. Communications in Computer and Information Science. Springer, pp. 380-389. DOI: 10.1007/978-3-030-43887-6_31.
Branco, P., L. Torgo and R. P. Ribeiro (2018). “MetaUtil: Meta Learning for Utility Maximization in Regression”. In: Discovery Science - 21st International Conference, DS 2018, Proceedings. Springer, pp. 129-143. DOI: 10.1007/978-3-030-01771-2_9.
Moniz, N., R. P. Ribeiro, V. Cerqueira and N. Chawla (2018). “SMOTEBoost for Regression: Improving the Prediction of Extreme Values”. In: 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018. IEEE, pp. 150-159. DOI: 10.1109/DSAA.2018.00025.
Branco, P., L. Torgo, R. P. Ribeiro, E. Frank, B. Pfahringer and M. M. Rau (2017). “Learning Through Utility Optimization in Regression Tasks”. In: 2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017. IEEE, pp. 30-39. DOI: 10.1109/DSAA.2017.63.
Branco, P., L. Torgo and R. P. Ribeiro (2017). “Relevance-Based Evaluation Metrics for Multi-class Imbalanced Domains”. In: Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings, Part I. Springer, pp. 698-710. DOI: 10.1007/978-3-319-57454-7_54.
Pereira, P., R. P. Ribeiro and J. Gama (2014). “Failure Prediction-An Application in the Railway Industry”. In: Discovery Science - 17th International Conference, DS 2014, Proceedings. Carl Smith Best Student Paper Award sponsored by Yahoo! Research. Springer, pp. 264-275. DOI: 10.1007/978-3-319-11812-3_23.
Torgo, L., R. P. Ribeiro, B. Pfahringer and P. Branco (2013). “SMOTE for Regression”. In: Progress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Proceedings. Springer, pp. 378-389. DOI: 10.1007/978-3-642-40669-0_33.
Ribeiro, R. P. (2012). “Towards Utility Maximization in Regression”. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW). IEEE, pp. 179-186. DOI: 10.1109/ICDMW.2012.82.
Torgo, L. and R. P. Ribeiro (2009). “Precision and Recall for Regression”. In: Discovery Science - 12th International Conference, DS 2009, Proceedings. Springer, pp. 332-346. DOI: 10.1007/978-3-642-04747-3_26.
Torgo, L. and R. P. Ribeiro (2007). “Utility-Based Regression”. In: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Springer, pp. 597-604. DOI: 10.1007/978-3-540-74976-9_63.
Ribeiro, R. P. and L. Torgo (2006). “Rule-Based Prediction of Rare Extreme Values”. In: Discovery Science - 9th International Conference, DS 2006, Proceedings. Carl Smith Best Student Paper Award sponsored by Yahoo! Research. Springer, pp. 219-230. DOI: 10.1007/11893318_23.
Torgo, L. and R. P. Ribeiro (2003). “Predicting Outliers”. In: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2003), vol. 2838, pp. 447–458. Springer. DOI: 10.1007/978-3-540-39804-2_40.
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Guidi, L, A. F. Guerra, D. C. E. Bakker, C. Canchaya, E. Curry, F. Foglini, J. Irisson, K. Malde, C. T. Marshall, M. Obst, et al. (2020). Future Science Brief 6 of the European Marine Board: Big Data in Marine Science. DOI: 10.5281/zenodo.3755793.
Andrade, T, J. Gama, R. P. Ribeiro, W. Sousa, and A. Carvalho (2019). “Anomaly Detection in Sequential Data: Principles and Case Studies”. In: Wiley Encyclopedia of Electrical and Electronics Engineering, pp. 1-14. DOI: 10.1002/047134608X.W8382.
Branco, P, R. P. Ribeiro and L. Torgo (2016). “UBL: an R package for Utility-based Learning”. In: CoRR abs/1604.08079. URL: http://arxiv.org/abs/1604.08079.
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R. P. Ribeiro (2011) Utility-based Regression. PhD Thesis submitted to Faculty of Sciences of University of Porto to fulfill the the degree of doctor of Computer Science. [PDF] - In the context of my thesis, I have developed the UBA package.
R. P. Ribeiro (2004) Models of Prediction of Rare Phenomena (in Portuguese). MSc Thesis submitted to Faculty of Economics of University of Porto to fulfill the the degree of master of Artificial Intelligence and Computation. [PDF]