Abstract
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance of the most relevant cases for the user. This problem has been intensively studied for classification problems. Recently it was recognized that imbalanced domains occur in several other contexts and for a diversity of types of tasks. This paper focus on imbalanced regression tasks. Resampling strategies are among the most successful approaches to imbalanced domains. In this work we propose variants of existing resampling strategies that are able to take into account the information regarding the neighborhood of the examples. Instead of performing sampling uniformly, our proposals bias the strategies for reinforcing some regions of the data sets. In an extensive set of experiments we provide evidence of the advantage of introducing a neighborhood bias in the resampling strategies.
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Notes
- 1.
Further details regarding SmoteR algorithm can be obtained in [17].
- 2.
Further details available in [12].
- 3.
Available at http://www.dcc.fc.up.pt/~rpribeiro/uba/.
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Acknowledgments
This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. P. Branco is supported by a Ph.D. scholarship of FCT (PD/BD/105788/2014). Prof. L. Torgo would also like to thank the support of Projects NORTE-01-0145-FEDER-000036 and UTAP-ICDT/CTM-NAN/0025/2014.
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Branco, P., Torgo, L., Ribeiro, R.P. (2017). Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_42
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