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Random Oracles for Regression Ensembles

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 373)

Abstract

This paper considers the use of Random Oracles in Ensembles for regression tasks. A Random Oracle model (Kuncheva and Rodríguez, 2007) consists of a pair of models and a fixed randomly created “oracle” (in the case of the Linear Random Oracle, it is a hyperplane that divides the dataset in two during training and, once the ensemble is trained, decides which model to use). They can be used as the base model for any ensemble method. Previously, they have been used for classification. Here, the use of Random Oracles for regression is studied using 61 datasets, Regression Trees as base models and several ensemble methods: Bagging , Random Subspaces, AdaBoost.R2 and Iterated Bagging. For all the considered methods and variants, ensembles with Random Oracles are better than the corresponding version without the Oracles.

Keywords

Root Mean Square Error Regression Tree Ensemble Member Average Rank Random Oracle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.University of BurgosSpain

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