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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

2020

Using Property-Based Testing to Generate Feedback for C Programming Exercises

Authors
Vasconcelos, PB; Ribeiro, RP;

Publication
OpenAccess Series in Informatics

Abstract
This paper reports on the use of property-based testing for providing feedback to C programming exercises. Test cases are generated automatically from properties specified in a test script; this not only makes it possible to conduct many tests (thus potentially find more mistakes), but also allows simplifying failed tests cases automatically. We present some experimental validation gathered for an introductory C programming course during the fall semester of 2018 that show significant positive correlations between getting feedback during the semester and the student's results in the final exam. We also discuss some limitations regarding feedback for undefined behaviors in the C language. 2012 ACM Subject Classification Social and professional topics ! Student assessment; Software and its engineering ! Software testing and debugging; Software and its engineering ! Domain specific languages.

2020

Imbalanced regression and extreme value prediction

Authors
Ribeiro, RP; Moniz, N;

Publication
Machine Learning

Abstract
Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume each domain value as equally important. Second, standard evaluation metrics focus on assessing the performance of models on the most common values of data distributions. In this paper, we present an approach to tackle imbalanced regression tasks where the objective is to predict extreme (rare) values. We propose an approach to formalise such tasks and to optimise/evaluate predictive models, overcoming the factors mentioned and issues in related work. We present an automatic and non-parametric method to obtain relevance functions, building on the concept of relevance as the mapping of target values into non-uniform domain preferences. Then, we propose SERA, a new evaluation metric capable of assessing the effectiveness and of optimising models towards the prediction of extreme values while penalising severe model bias. An experimental study demonstrates how SERA provides valid and useful insights into the performance of models in imbalanced regression tasks. © 2020, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.

2019

The search of conditional outliers

Authors
Portel, E; Ribeire, RP; Gama, J;

Publication
INTELLIGENT DATA ANALYSIS

Abstract
There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly for two different purposes. On one hand, outliers are considered error measurement observations that should be removed from the analysis, e.g. robust statistics. On the other hand, outliers are the interesting observations, like in fraud detection, and should be modelled by some learning method. In this work, we start from the observation that outliers are affected by the so-called simpson paradox: a trend that appears in different groups of data but disappears or reverses when these groups are combined. Given a data set, we learn a regression tree. The tree grows by partitioning the data into groups more and more homogeneous of the target variable. At each partition defined by the tree, we apply a box plot on the target variable to detect outliers. We would expect that the deeper nodes of the tree would contain less and less outliers. We observe that some points previously signalled as outliers are no more signalled as such, but new outliers appear.

2019

Pre-processing approaches for imbalanced distributions in regression

Authors
Branco, P; Torgo, L; Ribeiro, RP;

Publication
NEUROCOMPUTING

Abstract
Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.

2019

ECML PKDD 2018 Workshops - DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers

Authors
Monreale, A; Alzate, C; Kamp, M; Krishnamurthy, Y; Paurat, D; Mouchaweh, MS; Bifet, A; Gama, J; Ribeiro, RP;

Publication
DMLE/IOTSTREAMING@PKDD/ECML

Abstract

Supervised
thesis

2019

Anticipation of Perturbances in Telco Services

Author
Tânia Margarida Marques Carvalho

Institution
UP-FCUP

2019

Exploratory Analysis of Meteorological Data

Author
Joel Agostinho Nunes Pinto de Sousa

Institution
UP-FCUP

2019

Payment Default Prediction in Telco Services

Author
Ricardo Dias Azevedo

Institution
UP-FCUP

2018

Forecasting Water Pollutants

Author
António Gonçalo Fontes Pinheiro

Institution
UP-FCUP

2018

Utility-based Predictive Analytics

Author
Paula Alexandra de Oliveira Branco

Institution
UP-FCUP