Model selection methodology in supervised learning with evolutionary computation
Biosystems, ISSN: 0303-2647, Vol: 72, Issue: 1, Page: 187-196
2003
- 34Citations
- 39Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations34
- Citation Indexes34
- 34
- CrossRef28
- Captures39
- Readers39
- 39
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0303264703001436; http://dx.doi.org/10.1016/s0303-2647(03)00143-6; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0344861965&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/14642667; http://linkinghub.elsevier.com/retrieve/pii/S0303264703001436; http://api.elsevier.com/content/article/PII:S0303264703001436?httpAccept=text/xml; http://api.elsevier.com/content/article/PII:S0303264703001436?httpAccept=text/plain; https://linkinghub.elsevier.com/retrieve/pii/S0303264703001436; http://dx.doi.org/10.1016/s0303-2647%2803%2900143-6; https://dx.doi.org/10.1016/s0303-2647%2803%2900143-6
Elsevier BV
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