Ensemble Construction via Designed Output Distortion

  • Stefan W. Christensen
Conference paper

DOI: 10.1007/3-540-44938-8_29

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2709)
Cite this paper as:
Christensen S.W. (2003) Ensemble Construction via Designed Output Distortion. In: Windeatt T., Roli F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg

Abstract

A new technique for generating regression ensembles is introduced in the present paper. The technique is based on earlier work on promoting model diversity through injection of noise into the outputs; it differs from the earlier methods in its rigorous requirement that the mean displacements applied to any data points output value be exactly zero.

It is illustrated how even the introduction of extremely large displacements may lead to prediction accuracy superior to that achieved by bagging.

It is demonstrated how ensembles of models with very high bias may have much better prediction accuracy than single models of the same bias-defying the conventional belief that ensembling high bias models is not purposeful.

Finally is outlined how the technique may be applied to classification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Stefan W. Christensen
    • 1
  1. 1.Department of ChemistryUniversity of SouthamptonSouthamptonUK

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