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Random subspacing for regression ensembles
Tsymbal, Alexey
TCD-CS-2004-06 In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression and Dynamic Selection. We show that for simple regression methods such as global linear regression and nearest neighbours, this is a more effective method than the popular ensemble methods of Bagging and Boosting. We demonstrate that the approach can be effective even when the ensemble size is small.
Keyword(s): Computer Science
Publication Date:
2004
Type: Report
Peer-Reviewed: Unknown
Language(s): English
Institution: Trinity College Dublin
Funder(s): Science Foundation Ireland
Citation(s): Tsymbal, Alexey. 'Random subspacing for regression ensembles'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2004-06, 2004, pp6
Publisher(s): Trinity College Dublin, Department of Computer Science
File Format(s): application/pdf
First Indexed: 2014-05-13 05:12:39 Last Updated: 2015-04-10 05:13:52