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Feature Ranking by Classification Accuracy Estimation of Multiple Data Samples

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  • Novoselova Natalia

    (United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Arkady Borisov3, Inese Polaka4, 3-4 Riga Technical University +375-17-2842092)

  • Tom Igor

    (United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Arkady Borisov3, Inese Polaka4, 3-4 Riga Technical University)

  • Borisov Arkady
  • Polaka Inese

    (Riga Technical University)

Abstract

This article considers the gene ranking algorithm for the microarray data. The rank vector is estimated by classifications of the random data samples. At each iteration, the ranks of genes participating in the successful classification become higher. Unlike other methods of feature selection, the proposed algorithm allows increasing the generality of the classification models by construction of the balanced training samples and taking into account the descriptiveness of the gene combinations by the subset estimation.

Suggested Citation

  • Novoselova Natalia & Tom Igor & Borisov Arkady & Polaka Inese, 2013. "Feature Ranking by Classification Accuracy Estimation of Multiple Data Samples," Information Technology and Management Science, Sciendo, vol. 16(1), pages 95-100, December.
    • Handle: RePEc:vrs:itmasc:v:16:y:2013:i:1:p:95-100:n:15
    DOI: 10.2478/itms-2013-0015
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    References listed on IDEAS

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    1. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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