|Author||Mendes-Moreira, Joo ♦ Soares, Carlos ♦ Jorge, Alpio Mrio ♦ Sousa, Jorge Freire De|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|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.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||ACM Computing Surveys (CSUR)|
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