Ensemble approaches for regression

作者：João Mendes‐Moreira, Carlos Soares, Alí­pio Jorge, Jorge Freire de Sousa · 发表于：ACM Computing Surveys · 年份：2012 · DOI：10.1145/2379776.2379786 · 被引用次数：678 · 研究领域：Data Stream Mining Techniques、Machine Learning and Data Classification、Anomaly Detection Techniques and Applications

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.