Ensemble Approaches for Regression: A Survey

被引：493
作者：
Mendes-Moreira, Joao [1 ]
Soares, Carlos [2 ]
Jorge, Alipio Mario [3 ]
De Sousa, Jorge Freire [4 ]
机构：
[1] Univ Porto, LIAAD INESC TEC, FEUP, Oporto, Portugal
[2] Univ Porto, INESC TEC, FEP, Oporto, Portugal
[3] Univ Porto, LIAAD INESC TEC, FCUP, Oporto, Portugal
[4] Univ Porto, INESC TEC, FEUP, Oporto, Portugal
来源：
ACM COMPUTING SURVEYS | 2012年 / 45卷 / 01期
关键词：
Performance; Standardization; Ensemble learning; multiple models; regression; supervised learning; neural networks; decision trees; support vector machines; k-nearest neighbors; NEURAL-NETWORK ENSEMBLES; SUPPORT VECTOR MACHINE; DYNAMIC INTEGRATION; COMBINING CLASSIFIERS; ROTATION FOREST; SELECTION; ALGORITHM; GRADIENT; COMBINATION; CLASSIFICATION;
D O I：
10.1145/2379776.2379786
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
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页数：40
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