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Detalhes

Detalhes

  • Nome

    Rita Paula Ribeiro
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
002
Publicações

2020

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

Autores
Vasconcelos, P; 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.

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.

2019

Pre-processing approaches for imbalanced distributions in regression

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

Publicação
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

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

Publicação
DMLE/IOTSTREAMING@PKDD/ECML

Abstract

2019

Anomaly Detection in Sequential Data: Principles and Case Studies

Autores
Andrade, T; Gama, J; Ribeiro, RP; Sousa, W; Carvalho, A;

Publicação
Wiley Encyclopedia of Electrical and Electronics Engineering

Abstract

Teses
supervisionadas

2019

Anticipation of Perturbances in Telco Services

Autor
Tânia Margarida Marques Carvalho

Instituição
UP-FCUP

2019

Exploratory Analysis of Meteorological Data

Autor
Joel Agostinho Nunes Pinto de Sousa

Instituição
UP-FCUP

2019

Payment Default Prediction in Telco Services

Autor
Ricardo Dias Azevedo

Instituição
UP-FCUP

2018

Forecasting Water Pollutants

Autor
António Gonçalo Fontes Pinheiro

Instituição
UP-FCUP

2018

Utility-based Predictive Analytics

Autor
Paula Alexandra de Oliveira Branco

Instituição
UP-FCUP