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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems
verfasst von : Paula Branco, Luís Torgo, Rita P. Ribeiro
Erschienen in: Progress in Artificial Intelligence
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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.