FEATURE SELECTION IN THE TASK OF MEDICAL DIAGNOSTICS ON MICROARRAY DATA

FEATURE SELECTION IN THE TASK OF MEDICAL DIAGNOSTICS ON MICROARRAY DATA

In tasks of modern biology, the numbers of attributes often exceed the numbers of objects by orders of magnitude. For the solution of such tasks, a Data Mining method based on using a new measure of similarity between objects in the form of the Function of Rival Similarity (FRiS) is offered. On this...

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Bibliographic Details
Main Authors: N. G. Zagoruiko, O. A. Kutnenko, I. A. Borisova, V. V. Dyubanov, D. A. Levanov, O. A. Zyranov
Format: Article
Language:English
Published: Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders 2015-01-01
Series:Вавиловский журнал генетики и селекции
Subjects:
gene expression
function of rival similarity
feature selection
pattern recognition
Online Access:https://vavilov.elpub.ru/jour/article/view/319
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Summary:In tasks of modern biology, the numbers of attributes often exceed the numbers of objects by orders of magnitude. For the solution of such tasks, a Data Mining method based on using a new measure of similarity between objects in the form of the Function of Rival Similarity (FRiS) is offered. On this basis, methods of quantitative estimation of compactness of patterns, construction of decision rules, and feature selection are developed. All these techniques are implemented in the FRiS-GRAD algorithm. The high efficiency of the algorithm is illustrated by results of solving the task of disease recognition on a microarray dataset.
ISSN:2500-3259

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