Ensemble approaches for regression-Reference-Cited by-同舟云学术

Ensemble approaches for regression

Published:2012-11 Issue:1 Volume:45 Page:1-40
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Mendes-Moreira João¹,Soares Carlos²,Jorge Alípio Mário³,Sousa Jorge Freire De⁴

Affiliation:

1. LIAAD-INESC TEC, FEUP, Universidade do Porto, Portugal

2. INESC TEC, FEP, Universidade do Porto, Portugal

3. LIAAD-INESC TEC, FCUP, Universidade do Porto, Portugal

4. INESC TEC, FEUP, Universidade do Porto, Portugal

Abstract

The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.

Funder

Rank!

Fundação para a Ciência e a Tecnologia

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/2379776.2379786

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