Abstract
In view of the active use of DNA microarrays in solving various problems in medicine, bioinformatics, and molecular biology, there is a growing need of data mining algorithms capable of handling tasks in which the number of analyzed objects is smaller than the number of attributes by orders of magnitude. However, most of the currently existing algorithms were originally not intended to solving such complex, ill-conditioned problems. We have developed an approach based on the idea of rival similarity, which makes it possible to develop algorithms better suited for this purpose. We proposed one such algorithm, FRiS-GRAD, which simultaneously solves the problem of recognizing and selecting the system of informative features. Its efficiency is illustrated in a variety of medical problems compared to the most popular algorithms for the selection of informative features and recognition.
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Original Russian Text © N.G. Zagoruiko, O.A. Kutnenko, I.A. Borisova, V.V. Dyubanov, D.A. Levanov, O.A. Zyranov, 2014, published in Vavilovskii Zhurnal Genetiki i Selektsii, 2014, Vol. 18, No. 4/2, pp. 898–903.
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Zagoruiko, N.G., Kutnenko, O.A., Borisova, I.A. et al. Feature selection in the task of medical diagnostics on microarray data. Russ J Genet Appl Res 5, 330–334 (2015). https://doi.org/10.1134/S2079059715040164
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DOI: https://doi.org/10.1134/S2079059715040164