Model selection methodology in supervised learning with evolutionary computation.
The expressive power, powerful search capability, and the explicit nature of the resulting models make evolutionary methods very attractive for supervised learning applications in bioinformatics. However, their characteristics also make them highly susceptible to overtraining or to discovering chance relationships in the data. Identification of appropriate criteria for terminating evolution and for selecting an appropriately validated model is vital. Some approaches that are commonly applied to other modelling methods are not necessarily applicable in a straightforward manner to evolutionary methods. An approach to model selection is presented that is not unduly computationally intensive. To illustrate the issues and the technique two bioinformatic datasets are used, one relating to metabolite determination and the other to disease prediction from gene expression data.
Rowland
J J
JJ
Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB Wales, UK. jjr@aber.ac.uk
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D020869:Gene Expression Profiling / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D005875:Gibberellins / Q000378:metabolism; D007938:Leukemia / Q000175:diagnosis / Q000235:genetics*; D008954:Models, Biological; D012680:Sensitivity and Specificity; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods
D005875:Gibberellins; C007842:gibberellic acid