Generalisation and Model Selection in Supervised Learning with Evolutionary Computation

Research output: Contribution to conferencePaper

16 Citations (SciVal)

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 difficult. This paper reviews the issues and various approaches, illustrating salient points with examples taken from applications in bioinformatics.
Original languageEnglish
Pages119-130
Number of pages12
DOIs
Publication statusPublished - 2003

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