Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Branco, P (Author) Other; Torgo, L (Author) FCUP; Rita Ribeiro (Author) FCUP

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Pages: 513-524

18th EPIA Conference on Artificial Intelligence (EPIA)

Univ Porto, Fac Engn, Porto, PORTUGAL, SEP 05-08, 2017

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00M-YK5

DOI: 10.1007/978-3-319-65340-2_42

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12