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Author Mendes-Moreira, Joo ♦ Soares, Carlos ♦ Jorge, Alpio Mrio ♦ Sousa, Jorge Freire De
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Ensemble learning ♦ Decision trees ♦ K-nearest neighbors ♦ Multiple models ♦ Neural networks ♦ Regression ♦ Supervised learning ♦ Support vector machines
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.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-12-07
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 45
Issue Number 1
Page Count 40
Starting Page 1
Ending Page 40

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Source: ACM Digital Library