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Progress in Artificial Intelligence

, Volume 5, Issue 2, pp 91–103 | Cite as

Random feature weights for regression trees

  • Álvar Arnaiz-González
  • José F. Díez-Pastor
  • César García-Osorio
  • Juan J. Rodríguez
Regular Paper

Abstract

Ensembles are learning methods the operation of which relies on a combination of different base models. The diversity of ensembles is a fundamental aspect that conditions their operation. Random Feature Weights (\({\mathcal {RFW}}\)) was proposed as a classification-tree ensemble construction method in which diversity is introduced into each tree by means of a random weight associated with each attribute. These weights vary from one tree to another in the ensemble. In this article, the idea of \({\mathcal {RFW}}\) is adapted to decision-tree regression. A comparison is drawn with other ensemble construction methods: Bagging, Random Forest, Iterated Bagging, Random Subspaces and AdaBoost.R2 obtaining competitive results.

Keywords

Regression trees Ensembles Bagging Decision trees Random feature weights 

Notes

Acknowledgments

This work was funded by the Ministry of Economy and Competitiveness, project TIN 2011-24046.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Álvar Arnaiz-González
    • 1
  • José F. Díez-Pastor
    • 1
  • César García-Osorio
    • 1
  • Juan J. Rodríguez
    • 1
  1. 1.University of BurgosBurgosSpain

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