Rotation Forests for regression

DOI10.1016/j.amc.2013.03.139zbMath1290.68106OpenAlexW2041258712MaRDI QIDQ2016344

César García-Osorio, Carlos Pardo, Juan J. Rodríguez, José F. Diez-Pastor

Publication date: 20 June 2014

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2013.03.139

zbMATH Keywords

regressionbaggingboostingensemblesrandom subspacesRotation Forest

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• WEKA
• AdaBoost.MH
• AdaBoost.RT
• C4.5

Cites Work

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• Bagging predictors
• A decision-theoretic generalization of on-line learning and an application to boosting
• Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
• Inference for the generalization error
• An empirical study of using Rotation Forest to improve regressors
• Combining Pattern Classifiers
• Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
• Using iterated bagging to debias regressions

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