Predicting Harmful Algae Blooms. - Rita P. Ribeiro; Luís Torgo (EPIA, 2003)

Predicting Outliers. - Luís Torgo; Rita P. Ribeiro (PKDD, 2003)

Rule-Based Prediction of Rare Extreme Values. - Rita P. Ribeiro; Luís Torgo (Discovery Science, 2006)

Predicting Rare Extreme Values. - Luís Torgo; Rita P. Ribeiro (PAKDD, 2006)

Utility-Based Regression. - Luís Torgo; Rita P. Ribeiro (PKDD, 2007)

Precision and Recall for Regression. - Luís Torgo; Rita P. Ribeiro (Discovery Science, 2009)

Interval Forecast of Water Quality Parameters. - Orlando Ohashi; Luís Torgo; Rita P. Ribeiro (ECAI, 2010)

Towards Utility Maximization in Regression. - Rita P. Ribeiro (ICDM Workshops, 2012)

SMOTE for Regression. - Luís Torgo; Rita P. Ribeiro; Bernhard Pfahringer; Paula Branco (EPIA, 2013)

Failure Prediction - An Application in the Railway Industry. - Pedro Mota Pereira; Rita P. Ribeiro; João Gama 001 (Discovery Science, 2014)

An Experimental Study on Predictive Models Using Hierarchical Time Series. - Ana M. Silva; Rita P. Ribeiro; João Gama 001 (EPIA, 2015)

A Survey of Predictive Modelling under Imbalanced Distributions. - Paula Branco; Luís Torgo; Rita P. Ribeiro (CoRR, 2015)

Resampling strategies for regression. - Luís Torgo; Paula Branco; Rita P. Ribeiro; Bernhard Pfahringer (Expert Syst. J. Knowl. Eng., 2015)

Detection of Fraud Symptoms in the Retail Industry. - Rita P. Ribeiro; Ricardo Oliveira; João Gama 001 (IBERAMIA, 2016)

UBL: an R package for Utility-based Learning. - Paula Branco; Rita P. Ribeiro; Luís Torgo (CoRR, 2016)

A Survey of Predictive Modeling on Imbalanced Domains. - Paula Branco; Luís Torgo; Rita P. Ribeiro (ACM Comput. Surv., 2016)

Sequential anomalies: a study in the Railway Industry. - Rita P. Ribeiro; Pedro Mota Pereira; João Gama 001 (Mach. Learn., 2016)

Learning Through Utility Optimization in Regression Tasks. - Paula Branco; Luís Torgo; Rita P. Ribeiro; Eibe Frank; Bernhard Pfahringer; Markus Michael Rau (DSAA, 2017)

Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems. - Paula Branco; Luís Torgo; Rita P. Ribeiro (EPIA, 2017)

Outliers and the Simpson's Paradox. - Eduarda Portela; Rita P. Ribeiro; João Gama 001 (MICAI (1), 2017)

Relevance-Based Evaluation Metrics for Multi-class Imbalanced Domains. - Paula Branco; Luís Torgo; Rita P. Ribeiro (PAKDD (1), 2017)

SMOGN: a Pre-processing Approach for Imbalanced Regression. - Paula Branco; Luís Torgo; Rita P. Ribeiro (LIDTA@PKDD/ECML, 2017)

MetaUtil: Meta Learning for Utility Maximization in Regression. - Paula Branco; Luís Torgo; Rita P. Ribeiro (DS, 2018)

SMOTEBoost for Regression: Improving the Prediction of Extreme Values. - Nuno Moniz; Rita P. Ribeiro; Vítor Cerqueira; Nitesh V. Chawla (DSAA, 2018)

REBAGG: REsampled BAGGing for Imbalanced Regression. - Paula Branco; Luís Torgo; Rita P. Ribeiro (LIDTA@ECML/PKDD, 2018)

Resampling with neighbourhood bias on imbalanced domains. - Paula Branco; Luís Torgo; Rita P. Ribeiro (Expert Syst. J. Knowl. Eng., 2018)

A Study on Imbalanced Data Streams. - Ehsan Aminian; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (2), 2019)

The search of conditional outliers. - Eduarda Portela; Rita P. Ribeiro; João Gama 001 (Intell. Data Anal., 2019)

Pre-processing approaches for imbalanced distributions in regression. - Paula Branco; Luís Torgo; Rita P. Ribeiro (Neurocomputing, 2019)

Using Property-Based Testing to Generate Feedback for C Programming Exercises. - Pedro B. Vasconcelos; Rita P. Ribeiro (ICPEC, 2020)

Failure Detection of an Air Production Unit in Operational Context. - Mariana Barros; Bruno Veloso; Pedro Mota Pereira; Rita P. Ribeiro; João Gama 001 (IoT Streams/ITEM@PKDD/ECML, 2020)

Imbalanced regression and extreme value prediction. - Rita P. Ribeiro; Nuno Moniz (Mach. Learn., 2020)

Current Trends in Learning from Data Streams. - João Gama 001; Bruno Veloso; Ehsan Aminian; Rita P. Ribeiro (BDA, 2021)

Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. - Narjes Davari; Bruno Veloso; Rita P. Ribeiro; Pedro Mota Pereira; João Gama 001 (DSAA, 2021)

Improving Smart Waste Collection Using AutoML. - Sónia Teixeira; Guilherme Londres; Bruno Veloso; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (2), 2021)

Chebyshev approaches for imbalanced data streams regression models. - Ehsan Aminian; Rita P. Ribeiro; João Gama 001 (Data Min. Knowl. Discov., 2021)

A Survey on Data-Driven Predictive Maintenance for the Railway Industry. - Narjes Davari; Bruno Veloso; Gustavo de Assis Costa; Pedro Mota Pereira; Rita P. Ribeiro; João Gama 001 (Sensors, 2021)

Model Optimization in Imbalanced Regression. - Anibal Silva; Rita P. Ribeiro; Nuno Moniz (DS, 2022)

A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. - Narjes Davari; Sepideh Pashami; Bruno Veloso; Slawomir Nowaczyk; Yuantao Fan; Pedro Mota Pereira; Rita P. Ribeiro; João Gama 001 (IDA, 2022)

Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph. - Nirbhaya Shaji; João Gama 001; Rita P. Ribeiro; Pedro Gomes (IDA, 2022)

Combining Multiple Data Sources to Predict IUCN Conservation Status of Reptiles. - Nádia Soares; João F. Gonçalves; Raquel Vasconcelos; Rita P. Ribeiro (IDA, 2022)

Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. - Sérgio M. Jesus; José Pombal; Duarte M. Alves; André Ferreira Cruz; Pedro Saleiro; Rita P. Ribeiro; João Gama 001; Pedro Bizarro (NeurIPS, 2022)

Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. - Narjes Davari; Bruno Veloso; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (2), 2022)

Online Anomaly Explanation: A Case Study on Predictive Maintenance. - Rita P. Ribeiro; Saulo Martiello Mastelini; Narjes Davari; Ehsan Aminian; Bruno Veloso; João Gama 001 (PKDD/ECML Workshops (2), 2022)

Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data. - Rodrigo Salles; Jérôme Mendes; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (1), 2022)

Study on Correlation Between Vehicle Emissions and Air Quality in Porto. - Nirbhaya Shaji; Thiago Andrade; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (1), 2022)

An Online Data-Driven Predictive Maintenance Approach for Railway Switches. - Emanuel Sousa Tomé; Rita P. Ribeiro; Bruno Veloso; João Gama 001 (PKDD/ECML Workshops (2), 2022)

A Benchmark dataset for predictive maintenance. - Bruno Veloso; João Gama 001; Rita P. Ribeiro; Pedro Mota Pereira (CoRR, 2022)

Data-Driven Predictive Maintenance. - João Gama 001; Rita P. Ribeiro; Bruno Veloso (IEEE Intell. Syst., 2022)

MetroPT: A Benchmark dataset for predictive maintenance. - Bruno Veloso; João Gama 001; Rita P. Ribeiro; Pedro Pereira 004 (Zenodo, 2022)

MetroPT2: A Benchmark dataset for predictive maintenance. - Bruno Veloso; João Gama 001; Rita P. Ribeiro; Pedro Pereira 004 (Zenodo, 2022)

Topic Model with Contextual Outlier Handling: a Study on Electronic Invoice Product Descriptions. - César Andrade; Rita P. Ribeiro; João Gama 001 (EPIA (1), 2023)

Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis. - Thiago Andrade; Nirbhaya Shaji; Rita P. Ribeiro; João Gama 001 (EPIA (1), 2023)

XAI for Predictive Maintenance. - João Gama 001; Slawomir Nowaczyk; Sepideh Pashami; Rita P. Ribeiro; Grzegorz J. Nalepa; Bruno Veloso (KDD, 2023)

Data Science for Fighting Environmental Crime. - Marta Barbosa; Carolina Ribeiro; Filipe Gomes; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (2), 2023)

Anomaly Detection in Pet Behavioural Data. - Inês Oliveira e Silva; Rita P. Ribeiro; João Gama 001 (PKDD/ECML Workshops (2), 2023)

Why Industry 5.0 Needs XAI 2.0? - Szymon Bobek; Slawomir Nowaczyk; João Gama 001; Sepideh Pashami; Rita P. Ribeiro; Zahra Taghiyarrenani; Bruno Veloso; Lala H. Rajaoarisoa; Maciej Szelazek; Grzegorz J. Nalepa (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2023)

Explainable Predictive Maintenance. - Sepideh Pashami; Slawomir Nowaczyk; Yuantao Fan; Jakub Jakubowski; Nuno Paiva; Narjes Davari; Szymon Bobek; Samaneh Jamshidi; Hamid Sarmadi; Abdallah Alabdallah; Rita P. Ribeiro; Bruno Veloso; et al. (CoRR, 2023)

An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems. - Emanuel Sousa Tomé; Rita P. Ribeiro; Inês Dutra; Arlete Rodrigues (Sensors, 2023)

MetroPT-3 Dataset. - Narjes Davari; Bruno Veloso; Rita P. Ribeiro; João Gama 001 (UCI Machine Learning Repository, 2023)

Evaluating Short Text Stream Clustering on Large E-commerce Datasets. - César Andrade; Rita P. Ribeiro; João Gama 001 (BRACIS (3), 2024)

Community-Based Topic Modeling with Contextual Outlier Handling. - César Andrade; Rita P. Ribeiro; João Gama 001 (CAEPIA, 2024)

More (Enough) Is Better: Towards Few-Shot Illegal Landfill Waste Segmentation. - Matías Molina; Bruno Veloso; Carlos Abreu Ferreira; Rita P. Ribeiro; João Gama 001 (ECAI, 2024)

Predictive Maintenance for Industry 4.0 & 5.0. - Rita P. Ribeiro (EXPLAINS, 2024)

Emotion-Enhanced Pain Assessment Protocol. - Bruna Alves; Ana Almeida; Catarina Silva 001; Daniela Pais; Rita P. Ribeiro; João Gama 001; José Maria Fernandes; Susana Brás; Raquel Sebastião (HAR, 2024)

Super-Resolution Analysis for Landfill Waste Classification. - Matías Molina; Rita P. Ribeiro; Bruno Veloso; João Gama 001 (IDA (1), 2024)

Detecting and Explaining Anomalies in the Air Production Unit of a Train. - Narjes Davari; Bruno Veloso; Rita Paula Ribeiro; João Manuel Portela da Gama (SAC, 2024)

Towards Evaluation of Explainable Artificial Intelligence in Streaming Data. - Maciej Mozolewski; Szymon Bobek; Rita P. Ribeiro; Grzegorz J. Nalepa; João Gama 001 (xAI (4), 2024)

A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance. - João Gama 001; Rita P. Ribeiro; Saulo Martiello Mastelini; Narjes Davari; Bruno Veloso (CoRR, 2024)

Aequitas Flow: Streamlining Fair ML Experimentation. - Sérgio M. Jesus; Pedro Saleiro; Inês Oliveira e Silva; Beatriz M. Jorge; Rita P. Ribeiro; João Gama 001; Pedro Bizarro; Rayid Ghani (CoRR, 2024)

Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey. - Jakub Jakubowski; Natalia Wojak-Strzelecka; Rita P. Ribeiro; Sepideh Pashami; Szymon Bobek; João Gama 001; Grzegorz J. Nalepa (CoRR, 2024)

From fault detection to anomaly explanation: A case study on predictive maintenance. - João Gama 001; Rita P. Ribeiro; Saulo Martiello Mastelini; Narjes Davari; Bruno Veloso (J. Web Semant., 2024)

Efficient Instance Selection in Tree-Based Models for Data Streams Classification. - Aldo Marcelo Paim; João Gama 001; Bruno Veloso; Fabrício Enembreck; Rita P. Ribeiro (SAC, 2025)

Histogram approaches for imbalanced data streams regression. - Ehsan Aminian; João Gama 001; Rita P. Ribeiro (CoRR, 2025)

CART-based Synthetic Tabular Data Generation for Imbalanced Regression. - António Pedro Pinheiro; Rita P. Ribeiro (CoRR, 2025)