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de interesse
Detalhes

Detalhes

  • Nome

    Rita Paula Ribeiro
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
004
Publicações

2020

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

Autores
Vasconcelos, PB; Ribeiro, RP;

Publicação
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

Autores
Ribeiro, RP; Moniz, N;

Publicação
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.

2020

Failure Detection of an Air Production Unit in Operational Context

Autores
Barros, M; Veloso, B; Pereira, PM; Ribeiro, RP; Gama, J;

Publicação
Communications in Computer and Information Science - IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Abstract

2020

ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings

Autores
Koprinska, I; Kamp, M; Appice, A; Loglisci, C; Antonie, L; Zimmermann, A; Guidotti, R; Özgöbek, O; Ribeiro, RP; Gavaldà, R; Gama, J; Adilova, L; Krishnamurthy, Y; Ferreira, PM; Malerba, D; Medeiros, I; Ceci, M; Manco, G; Masciari, E; Ras, ZW; Christen, P; Ntoutsi, E; Schubert, E; Zimek, A; Monreale, A; Biecek, P; Rinzivillo, S; Kille, B; Lommatzsch, A; Gulla, JA;

Publicação
PKDD/ECML Workshops

Abstract

2019

The search of conditional outliers

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

Publicação
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.

Teses
supervisionadas

2020

Data Mining study on data collected in Arctic Oceanographic Campaigns

Autor
Tânia Isabel Alexandre Mestre Ferreira

Instituição
UP-FCUP

2019

Payment Default Prediction in Telco Services

Autor
Ricardo Dias Azevedo

Instituição
UP-FCUP

2019

Exploratory Analysis of Meteorological Data

Autor
Joel Agostinho Nunes Pinto de Sousa

Instituição
UP-FCUP

2019

Anticipation of Perturbances in Telco Services

Autor
Tânia Margarida Marques Carvalho

Instituição
UP-FCUP

2018

Utility-based Predictive Analytics

Autor
Paula Alexandra de Oliveira Branco

Instituição
UP-FCUP