Pre-processing approaches for imbalanced distributions in regression

• Type: Article in International Scientific Journal
• Date: 2019

Authors

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

Journal

Title: Neurocomputing

Vol. 343

Pages: 76-99

ISSN: 0925-2312

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 32 Citations

Scopus - 35 Citations

Other information

Authenticus ID: P-00Q-EWA

DOI: 10.1016/j.neucom.2018.11.100

Abstract (EN): Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24