Model aggregation methods and applications
Métodos de agregación de modelos y aplicaciones
| 2012 | |
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Agregación de modelos Boosting Bagging Random Forest Stacking Model Aggregation Boosting; Bagging Random Forest Stacking |
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| Español | |
| Universidad de Montevideo | |
| REDUM | |
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http://revistas.um.edu.uy/index.php/ingenieria/article/view/362
https://hdl.handle.net/20.500.12806/2517 |
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| Acceso abierto | |
| Atribución 4.0 Internacional |
| Sumario: | Aggregation methods in machine learning models combine several assumptions made on the same dataset in order to obtain a predictive model with higher accuracy. They have been extensively studied and have led to numerous experimental and theoretical works in various contexts: classification, regression, unsupervised learning, etc. The aim of this work is in a first time reviewing several known models of aggregation and then compares their performances over two applications. The first is for making predictions on different databases, particularly in multiclass problems, and the second to use them in the context of estimating the density of a random variable. |
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