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    About

    About

    I am a PhD student at the MAPi Doctoral Programme in Computer Science, hosted at Faculty of Sciences, University of Porto (FCUP) and the Laboratory of Artificial Intelligence and Decision Support (LIAAD – INESC TEC). I finished my BSc in Physics, in 2013, before I went on to get a MSc in Computer Science at the Faculty of Sciences, University of Porto, in 2015.

    My research currently focuses on predictive analytics for dependent data, supported financially by an FCT-MAPi grant. My advisors are Prof. Luís Torgo and Prof. Vítor Santos Costa.

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    Details

    Details

    • Name

      Mariana Rafaela Oliveira

    • Cluster

      Computer Science

    • Role

      External Student

    • Since

      01st February 2014
    Publications
    View all Publications

    2019

    Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

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

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

    Abstract

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    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

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    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

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    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.

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    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