Please use this identifier to cite or link to this item:
Title: Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems
Authors: Paula Oliveira Branco
Luís Torgo
Rita Paula Ribeiro
Issue Date: 2017
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. © Springer International Publishing AG 2017.
metadata.dc.type: conferenceObject
Appears in Collections:LIAAD - Articles in International Conferences

Files in This Item:
File Description SizeFormat 
P-00M-YK5.pdf327.1 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.