Google Scholar | ORCID | Scopus | DBLP
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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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Vasconcelos, P. B. and R. P. Ribeiro (2020). “Using Property-Based Testing to Generate Feedback for C Programming Exercises”. In: First International Computer Programming Education Conference (ICPEC 2020). Vol. 81. OpenAccess Series in Informatics (OASIcs). Schloss Dagstuhl-Leibniz-Zentrum für Informatik, pp. 28:1-28:10. DOI: 10.4230/OASIcs.ICPEC.2020.28.
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 and R. P. Ribeiro (2018). “REBAGG: REsampled BAGGing for Imbalanced Regression”. In: Second International Workshop on Learning with Imbalanced Domains: Theory and Applications (LIDTA). PMLR, pp. 67-81. URL: http://proceedings.mlr.press/v94/branco18a.html.
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). “Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems”. In: Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings.Springer, pp. 513-524. DOI: 10.1007/978-3-319-65340-2_42.
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, 2017, Proceedings, Part I. Springer, pp. 698-710. DOI: 10.1007/978-3-319-57454-7_54.
Ribeiro, R. P., R. Oliveira and J. Gama (2016). “Detection of Fraud Symptoms in the Retail Industry”. In: Advances in Artificial Intelligence - IBERAMIA 2016 - 15th Ibero-American Conference on AI Proceedings. Springer, pp. 189-200. DOI: 10.1007/978-3-319-47955-2_16.
Almeida, V., R. P. Ribeiro and J. Gama (2016). “Hierarchical Time Series Forecast in Electrical Grids”. In: _ICISA 2016: Proceedings of 7th International Conference on Information Science and Applications. Springer, pp. 995-1005. DOI: 10.1007/978-981-10-0557-2_95.
Silva, A. M., R. P. Ribeiro and J. Gama (2015). “An Experimental Study on Predictive Models Using Hierarchical Time Series”. In: EPIA 2015: Progress in Artificial Intelligence - Proceedings of 17th Portuguese Conference on Artificial Intelligence, Coimbra, Portugal. Springer, pp. 501-512. DOI: 10.1007/978-3-319-23485-4_50.
Pereira, P., R. P. Ribeiro and J. Gama “Failure Prediction-An Application in the Railway Industry”. In: DS 2014: Proceedings of 17th International Conference on Discovery Science. 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: EPIA 2013: Proceedings of 16th Portuguese Conference on Artificial Intelligence. Springer, pp. 378-389. DOI: 10.1007/978-3-642-40669-0_33.
Ribeiro, R. P. (2012). “Towards Utility Maximization in Regression”. In: Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on Data Mining. IEEE. , pp. 179-186. DOI: 10.1109/ICDMW.2012.82.
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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]