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Details

  • Name

    Paula Oliveira Branco
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    24th May 2013
Publications

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

The CURE for Class Imbalance

Authors
Bellinger, C; Branco, P; Torgo, L;

Publication
Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings

Abstract

2018

Resampling with neighbourhood bias on imbalanced domains

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

Publication
EXPERT SYSTEMS

Abstract
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance on the most relevant cases for the user. This problem has been extensively studied for classification problems, where the target variable is nominal. Recently, it was recognized that imbalanced domains occur in several other contexts and for multiple tasks, such as regression tasks, where the target variable is continuous. This paper focuses on imbalanced domains in both classification and regression tasks. Resampling strategies are among the most successful approaches to address imbalanced domains. In this work, we propose variants of existing resampling strategies that are able to take into account the information regarding the neighbourhood of the examples. Instead of performing sampling uniformly, our proposals bias the strategies to reinforce some regions of the data sets. With an extensive set of experiments, we provide evidence of the advantage of introducing a neighbourhood bias in the resampling strategies for both classification and regression tasks with imbalanced data sets.

2018

MetaUtil: Meta Learning for Utility Maximization in Regression

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

Publication
Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings

Abstract

2018

2nd Workshop on Learning with Imbalanced Domains: Preface

Authors
Torgo, L; Matwin, S; Japkowicz, N; Krawczyk, B; Moniz, N; Branco, P;

Publication
Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA@ECML/PKDD 2018, Dublin, Ireland, September 10, 2018

Abstract