Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Branco, P (Autor) Outra; Torgo, L (Autor) FCUP; Rita Ribeiro (Autor) FCUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Páginas: 513-524

18th EPIA Conference on Artificial Intelligence (EPIA)

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

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12