Отрывок: The description of population P2(t2) with number of generation t2 is possible to represent as a matrix form G. It is a possible degrees of input features. A is a matrix of coefficients of regression equations of candidates. And W is a matrix of coordinates. Elements of matrix W indicate the numbers of rows of the matrix G. In this case, each individual ξ ∈ P2(t2) with i = 1,m2 and j = 1, l of population size m2 contains information about the structure...
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dc.contributor.authorMokshin, V.V.-
dc.contributor.authorSaifudinov, I.R.-
dc.contributor.authorSharnin, L.M.-
dc.contributor.authorTrusfus, M.V.-
dc.contributor.authorTutubalin, P.I.-
dc.date.accessioned2018-05-14 17:27:09-
dc.date.available2018-05-14 17:27:09-
dc.date.issued2018-
dc.identifierDspace\SGAU\20180513\69145ru
dc.identifier.citationMokshin V.V A parallel genetic algorithm of feature selection for analysis of complex system/ MokshinV.V., SaifudinovI.R., SharninL.M., TrusfusM.V., TutubalinP.I. //Сборник трудов IV международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2018) - Самара: Новая техника, 2018. - С.2874-2883ru
dc.identifier.urihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/A-parallel-genetic-algorithm-of-feature-selection-for-analysis-of-complex-system-69145-
dc.description.abstractThe paper shows an approach of important features selection characterizing the evolution of the complex system. A parallel genetic algorithm is proposed to solve this problem. As a result of the proposed approach, the search for the best number of parallel evolutionary paths for the feature selection is carried out. The effectiveness of the proposed approach is demonstrated on the basis of the data analysis of production enterprise functioning. This paper also shows comparison results of parallel genetic algorithm with other algorithms of feature selection by standard deviation, Fisher criterion and multiple determination coefficient.ru
dc.language.isoenru
dc.publisherНовая техникаru
dc.subjectcomplex systemsru
dc.subjectfeature selectionru
dc.subjectparallel genetic algorithmru
dc.subjectregression modelru
dc.titleA parallel genetic algorithm of feature selection for analysis of complex systemru
dc.typeArticleru
dc.textpartThe description of population P2(t2) with number of generation t2 is possible to represent as a matrix form G. It is a possible degrees of input features. A is a matrix of coefficients of regression equations of candidates. And W is a matrix of coordinates. Elements of matrix W indicate the numbers of rows of the matrix G. In this case, each individual ξ ∈ P2(t2) with i = 1,m2 and j = 1, l of population size m2 contains information about the structure...-
Располагается в коллекциях: Информационные технологии и нанотехнологии

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