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About

About

I got a BSc in Physics before I went on to get a MSc in Computer Science at the Faculty of Sciences, University of Porto. I am now working towards a PhD in Informatics through the MAP-I program.

My research interests are mostly in Data Mining / Machine Learning, focusing on predictive analytics for dependent data.

Interest
Topics
Details

Details

  • Name

    Mariana Rafaela Oliveira
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    01st February 2014
Publications

2019

Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

Authors
Oliveira, M; Moniz, N; Torgo, L; Santos Costa, V;

Publication
2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Abstract

2018

Evaluation Procedures for Forecasting with Spatio-Temporal Data

Authors
Oliveira, M; Torgo, L; Costa, VS;

Publication
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Abstract

2017

Dynamic and Heterogeneous Ensembles for Time Series Forecasting

Authors
Cerqueira, V; Torgo, L; Oliveira, M; Pfahringer, B;

Publication
2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017

Abstract

2016

Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches

Authors
Oliveira, M; Torgo, L; Costa, VS;

Publication
DISCOVERY SCIENCE, (DS 2016)

Abstract
We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually.

2016

Development of an autonomous system for integrated marine monitoring

Authors
Catarina, M; Ana Paula, M; Maria, C; Hugo, R; Cristina, A; Isabel, A; Sandra, R; Teresa, B; Sérgio, L; Antonina, DS; Alexandra, S; Cátia, B; Sónia, C; Raquel, M; Catarina, C; André, D; Hugo, F; Ireneu, D; Luís, T; Mariana, O; Nuno, D; Pedro, J; Alfredo, M; Eduardo, S;

Publication
Frontiers in Marine Science

Abstract