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Strangeness Minimisation Feature Selection with Confidence Machines

  • Tony Bellotti
  • Zhiyuan Luo
  • Alex Gammerman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

In this paper, we focus on the problem of feature selection with confidence machines (CM). CM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We present a new feature selection method, namely Strangeness Minimisation Feature Selection, designed for CM. We apply this feature selection method to the problem of microarray classification and demonstrate its effectiveness.

Keywords

Feature Selection Feature Subset Feature Selection Method Feature Selection Technique Region Prediction 
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 2006

Authors and Affiliations

  • Tony Bellotti
    • 1
  • Zhiyuan Luo
    • 1
  • Alex Gammerman
    • 1
  1. 1.Computer Learning Research Centre, Royal HollowayUniversity of LondonEgham, SurreyUK

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