Show simple item record Rowland, Jeremy John 2006-04-25T15:22:09Z 2006-04-25T15:22:09Z 2003-11
dc.identifier.citation Rowland , J J 2003 , ' Model Selection Methodology in Supervised Learning with Evolutionary Computation ' BioSystems , vol 72 , no. 1-2 , pp. 187-196 . DOI: 10.1016/S0303-2647(03)00143-6 en
dc.identifier.issn 0303-2647
dc.identifier.other PURE: 68347
dc.identifier.other PURE UUID: d011ff7a-8a30-414f-b912-574ff2f82005
dc.identifier.other dspace: 2160/146
dc.identifier.other DSpace_20121128.csv: row: 118
dc.identifier.other Scopus: 0344861965
dc.description Rowland, J.J. (2003) Model Selection Methodology in Supervised Learning with Evolutionary Computation. BioSystems 72, 1-2, pp 187-196, Nov en
dc.description.abstract 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. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof BioSystems en
dc.rights en
dc.subject validation en
dc.subject genetic programming en
dc.subject gene expression en
dc.subject model selection en
dc.subject generalisation en
dc.title Model Selection Methodology in Supervised Learning with Evolutionary Computation en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en

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