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Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems

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  • Pavel Pudil
  • Petr Somol

Abstract

We provide an overview of problems related to variable selection (also known as feature selection) techniques in decision-making problems based on machine learning with a particular emphasis on recent knowledge. Several popular methods are reviewed and assigned to a taxonomical context. Issues related to the generalization-versus-performance trade-off, inherent in currently used variable selection approaches, are addressed and illustrated on real-world examples.

Suggested Citation

  • Pavel Pudil & Petr Somol, 2008. "Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems," Acta Oeconomica Pragensia, University of Economics, Prague, vol. 2008(4), pages 37-55.
  • Handle: RePEc:prg:jnlaop:v:2008:y:2008:i:4:id:131:p:37-55
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    Citations

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    Cited by:

    1. Dernoncourt, David & Hanczar, Blaise & Zucker, Jean-Daniel, 2014. "Analysis of feature selection stability on high dimension and small sample data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 681-693.

    More about this item

    Keywords

    variable selection; feature selection; machine learning; decision rules; classification;

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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