Feature Ranking by Classification Accuracy Estimation of Multiple Data Samples

Novoselova Natalia (), Tom Igor (), Borisov Arkady () and Polaka Inese ()
Additional contact information
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
Polaka Inese: Riga Technical University

Information Technology and Management Science, 2013, vol. 16, issue 1, 95-100

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.

Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/itms-2013-0015 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:itmasc:v:16:y:2013:i:1:p:95-100:n:15

DOI: 10.2478/itms-2013-0015

Access Statistics for this article

Information Technology and Management Science is currently edited by J. Merkurjevs

More articles in Information Technology and Management Science from Sciendo
Bibliographic data for series maintained by Peter Golla ().