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About

I received my PhD degree in Computer Science from the University of Porto, Portugal in 2011.
Currently, I'm an assistant professor at the Department of Computer Science of the Faculty of Sciences of the University of Porto and member of LIAAD-INESC TEC, the Artificial Intelligence and Decision Support Lab of University of Porto.
My main research interests include Data Mining and Machine Learning, in particular outlier detection, novelty detection, utility-based learning and evaluation issues on learning tasks.
As a member of LIAAD-INESC TEC, I have been involved in several research projects concerning environmental applications, fraud detection and fault diagnosis. I have also been member of the program committee for several conferences, serving as reviewer of several journals and involved in the organization of some scientific events.

Interest
Topics
Details

Details

  • Name

    Rita Paula Ribeiro
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2008
004
Publications

2021

A Survey on Data-Driven Predictive Maintenance for the Railway Industry

Authors
Davari, N; Veloso, B; Costa, GD; Pereira, PM; Ribeiro, RP; Gama, J;

Publication
SENSORS

Abstract
In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

2021

Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry

Authors
Davari, N; Veloso, B; Ribeiro, RP; Pereira, PM; Gama, J;

Publication
8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021, Porto, Portugal, October 6-9, 2021

Abstract

2021

Current Trends in Learning from Data Streams

Authors
Gama, J; Veloso, B; Aminian, E; Ribeiro, RP;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
This article presents our recent work on the topic of learning from data streams. We focus on emerging topics, including fraud detection, learning from rare cases, and hyper-parameter tuning for streaming data. © 2021, Springer Nature Switzerland AG.

2021

Chebyshev approaches for imbalanced data streams regression models

Authors
Aminian, E; Ribeiro, RP; Gama, J;

Publication
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
In recent years data stream mining and learning from imbalanced data have been active research areas. Even though solutions exist to tackle these two problems, most of them are not designed to handle challenges inherited from both problems. As far as we are aware, the few approaches in the area of learning from imbalanced data streams fall in the context of classification, and no efforts on the regression domain have been reported yet. This paper proposes a technique that uses sampling strategies to cope with imbalanced data streams in a regression setting, where the most important cases have rare and extreme target values. Specifically, we employ under-sampling and over-sampling strategies that resort to Chebyshev's inequality value as a heuristic to disclose the type of incoming cases (i.e. frequent or rare). We have evaluated our proposal by applying it in the training of models by four well-known regression algorithms over fourteen benchmark data sets. We conducted a series of experiments with different setups on both synthetic and real-world data sets. The experimental results confirm our approach's effectiveness by showing the models' superior performance trained by each of the sampling strategies compared with their baseline pairs.

2020

A Study on Imbalanced Data Streams

Authors
Aminian, E; Ribeiro, RP; Gama, J;

Publication
Machine Learning and Knowledge Discovery in Databases - Communications in Computer and Information Science

Abstract

Supervised
thesis

2021

Evaluating Fairness, Explainability and Robustness of AI Systems

Author
Sérgio Gabriel Pontes de Jesus

Institution
UP-FCUP

2021

Análise de Séries Temporais de Dados Meteorológicos da Cidade do Porto

Author
Ana Catarina Pinheiro Monteiro

Institution
UP-FCUP

2021

Detecting fake behavior in online social networks using smartphone data

Author
Nirbhaya Shaji

Institution
UP-FCUP

2021

Predicting IUCN conservation status through machine learning: a case study on reptiles

Author
Nádia Filipa de Jesus Soares

Institution
UP-FCUP

2020

Data Mining study on data collected in Arctic Oceanographic Campaigns

Author
Tânia Isabel Alexandre Mestre Ferreira

Institution
UP-FCUP