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Pattern-based feature selection in genomics and proteomics

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  • Gabriela Alexe
  • Sorin Alexe
  • Peter Hammer
  • Bela Vizvari

Abstract

A major difficulty in bioinformatics is due to the size of the datasets, which contain frequently large numbers of variables. In this study, we present a two-step procedure for feature selection. In a first “filtering” stage, a relatively small subset of features is identified on the basis of several criteria. In the second stage, the importance of the selected variables is evaluated based on the frequency of their participation in relevant patterns and low impact variables are eliminated. This step is applied iteratively, until arriving to a Pareto-optimal “support set”, which balances the conflicting criteria of simplicity and accuracy. Copyright Springer Science+Business Media, LLC 2006

Suggested Citation

  • Gabriela Alexe & Sorin Alexe & Peter Hammer & Bela Vizvari, 2006. "Pattern-based feature selection in genomics and proteomics," Annals of Operations Research, Springer, vol. 148(1), pages 189-201, November.
    • Handle: RePEc:spr:annopr:v:148:y:2006:i:1:p:189-201:10.1007/s10479-006-0084-x
    DOI: 10.1007/s10479-006-0084-x
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    References listed on IDEAS

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    1. Sorin Alexe & Eugene Blackstone & Peter Hammer & Hemant Ishwaran & Michael Lauer & Claire Pothier Snader, 2003. "Coronary Risk Prediction by Logical Analysis of Data," Annals of Operations Research, Springer, vol. 119(1), pages 15-42, March.
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    Cited by:

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    2. Kedong Yan & Hong Seo Ryoo, 2017. "Strong valid inequalities for Boolean logical pattern generation," Journal of Global Optimization, Springer, vol. 69(1), pages 183-230, September.
    3. Kedong Yan & Hong Seo Ryoo, 2019. "A multi-term, polyhedral relaxation of a 0–1 multilinear function for Boolean logical pattern generation," Journal of Global Optimization, Springer, vol. 74(4), pages 705-735, August.

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