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Generalisation and Model Selection in Supervised Learning with Evolutionary Computation

  • Jem J. Rowland
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

EC-based supervised learning has been demonstrated to be an effective approach to forming predictive models in genomics, spectral interpretation, and other problems in modern biology. Longer-established methods such as PLS and ANN are also often successful. In supervised learning, overtraining is always a potential problem. The literature reports numerous methods of validating predictive models in order to avoid overtraining. Some of these approaches can be applied to EC-based methods of supervised learning, though the characteristics of EC learning are different from those obtained with PLS and ANN and selecting a suitably general model can be more dificult. This paper reviews the issues and various approaches, illustrating salient points with examples taken from applications in bioinformatics.

Keywords

Evolutionary Computation Supervise Learning Model Selection Criterion Sporadic Breast Cancer Linear Genetic Programming 
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 2003

Authors and Affiliations

  • Jem J. Rowland
    • 1
  1. 1.Dept. of Computer ScienceUniversity of Wales AberystwythWalesUK

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