Selecting informative variables in the identification problem
Eugene D. Mihov, Oleg V. Nepomnyashchiy
Institute of Space and Information Technology,
Siberian Federal University, Kirensky, 26, Krasnoyarsk, 660074, Russia
The problem of multidimensional object classification with small training sample is considered. The following algorithms of estimating variable informativeness are considered: Ad, Del, AdDel.
A new algorithm for selecting informative variables is proposed. It is based on the optimization of the coefficient vector of the kernel fuzziness. Some modification of this algorithm is also discussed.
The comparative analysis of existing methods for selecting informative variables is presented.
classification, small training sample, informative variable, optimization of the coefficient vector of the kernel fuzziness.
|Russian Science Foundation
|The study was performed by a grant from the Russian Science Foundation (project no. 16-19-10089).
PDF file (292 kB)
Received in revised form: 14.08.2016
Eugene D. Mihov, Oleg V. Nepomnyashchiy, “Selecting informative variables in the identification problem”, J. Sib. Fed. Univ. Math. Phys., 9:4 (2016), 473–480
Citation in format AMSBIB
\by Eugene~D.~Mihov, Oleg~V.~Nepomnyashchiy
\paper Selecting informative variables in the identification problem
\jour J. Sib. Fed. Univ. Math. Phys.
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